def run_algorithm():
unsupported_modes = ['random_slice', 'random_uniform']
algorithm = SGD(learning_rate,
cost,
batch_size=batch_size,
train_iteration_mode=mode,
monitoring_dataset=None,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
algorithm.setup(dataset=dataset, model=model)
raised = False
try:
algorithm.train(dataset)
except ValueError:
print mode
assert mode in unsupported_modes
raised = True
if mode in unsupported_modes:
assert raised
return True
return False
def run_algorithm():
unsupported_modes = ['random_slice', 'random_uniform']
algorithm = SGD(learning_rate,
cost,
batch_size=5,
train_iteration_mode=mode,
monitoring_dataset=None,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
algorithm.setup(dataset=dataset, model=model)
raised = False
try:
algorithm.train(dataset)
except ValueError:
print mode
assert mode in unsupported_modes
raised = True
if mode in unsupported_modes:
assert raised
return True
return False
def test_adadelta():
"""
Make sure that learning_rule.AdaDelta obtains the same parameter values as
with a hand-crafted AdaDelta implementation, given a dummy model and
learning rate scaler for each parameter.
Reference:
"AdaDelta: An Adaptive Learning Rate Method", Matthew D. Zeiler.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfOneHalfParamsSquared(), (0., DummyCost())])
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
decay = 0.95
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=AdaDelta(decay),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
state = {}
for param in model.get_params():
param_shape = param.get_value().shape
state[param] = {}
state[param]['g2'] = np.zeros(param_shape)
state[param]['dx2'] = np.zeros(param_shape)
def adadelta_manual(model, state):
inc = []
rval = []
for scale, param in izip(scales, model.get_params()):
pstate = state[param]
param_val = param.get_value()
# begin adadelta
pstate['g2'] = decay * pstate['g2'] + (1 - decay) * param_val ** 2
rms_g_t = np.sqrt(pstate['g2'] + scale * learning_rate)
rms_dx_tm1 = np.sqrt(pstate['dx2'] + scale * learning_rate)
dx_t = -rms_dx_tm1 / rms_g_t * param_val
pstate['dx2'] = decay * pstate['dx2'] + (1 - decay) * dx_t ** 2
rval += [param_val + dx_t]
return rval
manual = adadelta_manual(model, state)
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in izip(manual, model.get_params()))
manual = adadelta_manual(model, state)
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in
izip(manual, model.get_params()))
def test_adadelta():
"""
Make sure that learning_rule.AdaDelta obtains the same parameter values as
with a hand-crafted AdaDelta implementation, given a dummy model and
learning rate scaler for each parameter.
Reference:
"AdaDelta: An Adaptive Learning Rate Method", Matthew D. Zeiler.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfOneHalfParamsSquared(), (0., DummyCost())])
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
decay = 0.95
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=AdaDelta(decay),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
state = {}
for param in model.get_params():
param_shape = param.get_value().shape
state[param] = {}
state[param]['g2'] = np.zeros(param_shape)
state[param]['dx2'] = np.zeros(param_shape)
def adadelta_manual(model, state):
inc = []
rval = []
for scale, param in izip(scales, model.get_params()):
pstate = state[param]
param_val = param.get_value()
# begin adadelta
pstate['g2'] = decay * pstate['g2'] + (1 - decay) * param_val**2
rms_g_t = np.sqrt(pstate['g2'] + scale * learning_rate)
rms_dx_tm1 = np.sqrt(pstate['dx2'] + scale * learning_rate)
dx_t = -rms_dx_tm1 / rms_g_t * param_val
pstate['dx2'] = decay * pstate['dx2'] + (1 - decay) * dx_t**2
rval += [param_val + dx_t]
return rval
manual = adadelta_manual(model, state)
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in izip(manual, model.get_params()))
manual = adadelta_manual(model, state)
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in izip(manual, model.get_params()))
def test_lr_scalers():
"""
Tests that SGD respects Model.get_lr_scalers
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1, ), (9, ), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
learning_rate = .001
class ModelWithScalers(Model):
def __init__(self):
super(ModelWithScalers, self).__init__()
self._params = [sharedX(np.zeros(shape)) for shape in shapes]
self.input_space = VectorSpace(1)
def __call__(self, X):
# Implemented only so that DummyCost would work
return X
def get_lr_scalers(self):
return dict(zip(self._params, scales))
model = ModelWithScalers()
dataset = ArangeDataset(1)
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=Momentum(.0),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
manual = [
param - learning_rate * scale for param, scale in zip(manual, scales)
]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
manual = [
param - learning_rate * scale for param, scale in zip(manual, scales)
]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
def test_lr_scalers():
"""
Tests that SGD respects Model.get_lr_scalers
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1,), (9,), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
learning_rate = .001
class ModelWithScalers(Model):
def __init__(self):
super(ModelWithScalers, self).__init__()
self._params = [sharedX(np.zeros(shape)) for shape in shapes]
self.input_space = VectorSpace(1)
def __call__(self, X):
# Implemented only so that DummyCost would work
return X
def get_lr_scalers(self):
return dict(zip(self._params, scales))
model = ModelWithScalers()
dataset = ArangeDataset(1)
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=Momentum(.0),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
manual = [param - learning_rate * scale for param, scale in
zip(manual, scales)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in zip(manual, model.get_params()))
manual = [param - learning_rate * scale
for param, scale
in zip(manual, scales)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in zip(manual, model.get_params()))
def test_adagrad():
"""
Make sure that learning_rule.AdaGrad obtains the same parameter values as
with a hand-crafted AdaGrad implementation, given a dummy model and
learning rate scaler for each parameter.
Reference:
"Adaptive subgradient methods for online learning and
stochastic optimization", Duchi J, Hazan E, Singer Y.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfOneHalfParamsSquared(), (0., DummyCost())])
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=AdaGrad(),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
state = {}
for param in model.get_params():
param_shape = param.get_value().shape
state[param] = {}
state[param]['sg2'] = np.zeros(param_shape)
def adagrad_manual(model, state):
rval = []
for scale, param in izip(scales, model.get_params()):
pstate = state[param]
param_val = param.get_value()
# begin adadelta
pstate['sg2'] += param_val ** 2
dx_t = - (scale * learning_rate
/ np.sqrt(pstate['sg2'])
* param_val)
rval += [param_val + dx_t]
return rval
manual = adagrad_manual(model, state)
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in izip(manual, model.get_params()))
manual = adagrad_manual(model, state)
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in
izip(manual, model.get_params()))
def test_adagrad():
"""
Make sure that learning_rule.AdaGrad obtains the same parameter values as
with a hand-crafted AdaGrad implementation, given a dummy model and
learning rate scaler for each parameter.
Reference:
"Adaptive subgradient methods for online learning and
stochastic optimization", Duchi J, Hazan E, Singer Y.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfOneHalfParamsSquared(), (0., DummyCost())])
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=AdaGrad(),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
state = {}
for param in model.get_params():
param_shape = param.get_value().shape
state[param] = {}
state[param]['sg2'] = np.zeros(param_shape)
def adagrad_manual(model, state):
rval = []
for scale, param in izip(scales, model.get_params()):
pstate = state[param]
param_val = param.get_value()
# begin adadelta
pstate['sg2'] += param_val**2
dx_t = -(scale * learning_rate / np.sqrt(pstate['sg2']) *
param_val)
rval += [param_val + dx_t]
return rval
manual = adagrad_manual(model, state)
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in izip(manual, model.get_params()))
manual = adagrad_manual(model, state)
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in izip(manual, model.get_params()))
def test_lr_scalers_momentum():
"""
Tests that SGD respects Model.get_lr_scalers when using
momentum.
"""
cost = SumOfParams()
scales = [.01, .02, .05, 1., 5.]
shapes = [(1, ), (9, ), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
learning_rate = .001
class ModelWithScalers(Model):
def __init__(self):
self._params = [sharedX(np.zeros(shape)) for shape in shapes]
self.input_space = VectorSpace(1)
def get_lr_scalers(self):
return dict(zip(self._params, scales))
model = ModelWithScalers()
dataset = ArangeDataset(1)
momentum = 0.5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
init_momentum=momentum,
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
inc = [-learning_rate * scale for param, scale in zip(manual, scales)]
manual = [param + i for param, i in zip(manual, inc)]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
manual = [
param - learning_rate * scale + i * momentum
for param, scale, i in zip(manual, scales, inc)
]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
def test_rmsprop():
"""
Make sure that learning_rule.RMSProp obtains the same parameter values as
with a hand-crafted RMSProp implementation, given a dummy model and
learning rate scaler for each parameter.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfOneHalfParamsSquared(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1, ), (9, ), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
learning_rate = .001
decay = 0.90
max_scaling = 1e5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=RMSProp(decay),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
state = {}
for param in model.get_params():
param_shape = param.get_value().shape
state[param] = {}
state[param]['g2'] = np.zeros(param_shape)
def rmsprop_manual(model, state):
inc = []
rval = []
epsilon = 1. / max_scaling
for scale, param in izip(scales, model.get_params()):
pstate = state[param]
param_val = param.get_value()
# begin rmsprop
pstate['g2'] = decay * pstate['g2'] + (1 - decay) * param_val**2
rms_g_t = np.maximum(np.sqrt(pstate['g2']), epsilon)
dx_t = -scale * learning_rate / rms_g_t * param_val
rval += [param_val + dx_t]
return rval
manual = rmsprop_manual(model, state)
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in izip(manual, model.get_params()))
def test_rmsprop():
"""
Make sure that learning_rule.RMSProp obtains the same parameter values as
with a hand-crafted RMSProp implementation, given a dummy model and
learning rate scaler for each parameter.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfOneHalfParamsSquared(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1,), (9,), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
learning_rate = .001
decay = 0.90
max_scaling = 1e5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=RMSProp(decay),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
state = {}
for param in model.get_params():
param_shape = param.get_value().shape
state[param] = {}
state[param]['g2'] = np.zeros(param_shape)
def rmsprop_manual(model, state):
inc = []
rval = []
epsilon = 1. / max_scaling
for scale, param in izip(scales, model.get_params()):
pstate = state[param]
param_val = param.get_value()
# begin rmsprop
pstate['g2'] = decay * pstate['g2'] + (1 - decay) * param_val ** 2
rms_g_t = np.maximum(np.sqrt(pstate['g2']), epsilon)
dx_t = - scale * learning_rate / rms_g_t * param_val
rval += [param_val + dx_t]
return rval
manual = rmsprop_manual(model, state)
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in izip(manual, model.get_params()))
def test_nesterov_momentum():
"""
Make sure that learning_rule.Momentum obtains the same parameter values as
with a hand-crafted sgd w/ momentum implementation, given a dummy model and
learning rate scaler for each parameter.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
momentum = 0.5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=Momentum(momentum, nesterov_momentum=True),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
vel = [-learning_rate * scale for scale in scales]
updates = [
-learning_rate * scale + v * momentum
for scale, v in izip(scales, vel)
]
manual = [param + update for param, update in izip(manual, updates)]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in izip(manual, model.get_params()))
vel = [
-learning_rate * scale + i * momentum
for scale, i in izip(scales, vel)
]
updates = [
-learning_rate * scale + v * momentum
for scale, v in izip(scales, vel)
]
manual = [param + update for param, update in izip(manual, updates)]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in izip(manual, model.get_params()))
def test_sgd_sequential():
# tests that requesting train_iteration_mode = 'sequential'
# works
dim = 1
batch_size = 3
m = 5 * batch_size
dataset = ArangeDataset(m)
model = SoftmaxModel(dim)
learning_rate = 1e-3
batch_size = 5
visited = [False] * m
def visit(X):
assert X.shape[1] == 1
assert np.all(X[1:] == X[0:-1] + 1)
start = int(X[0, 0])
if start > 0:
assert visited[start - 1]
for i in xrange(batch_size):
assert not visited[start + i]
visited[start + i] = 1
data_specs = (model.get_input_space(), model.get_input_source())
cost = CallbackCost(visit, data_specs)
# We need to include this so the test actually stops running at some point
termination_criterion = EpochCounter(5)
algorithm = SGD(learning_rate,
cost,
batch_size=5,
train_iteration_mode='sequential',
monitoring_dataset=None,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
algorithm.setup(dataset=dataset, model=model)
algorithm.train(dataset)
assert all(visited)
def test_sgd_sequential():
# tests that requesting train_iteration_mode = 'sequential'
# works
dim = 1
batch_size = 3
m = 5 * batch_size
dataset = ArangeDataset(m)
model = SoftmaxModel(dim)
learning_rate = 1e-3
batch_size = 5
visited = [False] * m
def visit(X):
assert X.shape[1] == 1
assert np.all(X[1:] == X[0:-1]+1)
start = int(X[0, 0])
if start > 0:
assert visited[start - 1]
for i in xrange(batch_size):
assert not visited[start+i]
visited[start+i] = 1
data_specs = (model.get_input_space(), model.get_input_source())
cost = CallbackCost(visit, data_specs)
# We need to include this so the test actually stops running at some point
termination_criterion = EpochCounter(5)
algorithm = SGD(learning_rate,
cost,
batch_size=batch_size,
train_iteration_mode='sequential',
monitoring_dataset=None,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
algorithm.setup(dataset=dataset, model=model)
algorithm.train(dataset)
assert all(visited)
def test_lr_scalers_momentum():
"""
Tests that SGD respects Model.get_lr_scalers when using
momentum.
"""
cost = SumOfParams()
scales = [ .01, .02, .05, 1., 5. ]
shapes = [(1,), (9,), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
learning_rate = .001
class ModelWithScalers(Model):
def __init__(self):
self._params = [sharedX(np.zeros(shape)) for shape in shapes]
self.input_space = VectorSpace(1)
def get_lr_scalers(self):
return dict(zip(self._params, scales))
model = ModelWithScalers()
dataset = ArangeDataset(1)
momentum = 0.5
sgd = SGD(cost=cost, learning_rate=learning_rate, init_momentum=momentum,
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
inc = [ - learning_rate * scale for param, scale in
zip(manual, scales)]
manual = [param + i for param, i in zip(manual, inc)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value()) for manual_param,
sgd_param in zip(manual, model.get_params()))
manual = [param - learning_rate * scale + i * momentum for param, scale, i in
zip(manual, scales, inc)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value()) for manual_param,
sgd_param in zip(manual, model.get_params()))
def test_momentum():
"""
Make sure that learning_rule.Momentum obtains the same parameter values as
with a hand-crafted sgd w/ momentum implementation, given a dummy model and
learning rate scaler for each parameter.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1, ), (9, ), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
learning_rate = .001
momentum = 0.5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=Momentum(momentum),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
inc = [-learning_rate * scale for param, scale in zip(manual, scales)]
manual = [param + i for param, i in zip(manual, inc)]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
manual = [
param - learning_rate * scale + i * momentum
for param, scale, i in zip(manual, scales, inc)
]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
def test_lr_scalers_momentum():
"""
Tests that SGD respects Model.get_lr_scalers when using
momentum.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1, ), (9, ), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
learning_rate = .001
momentum = 0.5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
init_momentum=momentum,
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
inc = [-learning_rate * scale for param, scale in zip(manual, scales)]
manual = [param + i for param, i in zip(manual, inc)]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
manual = [
param - learning_rate * scale + i * momentum
for param, scale, i in zip(manual, scales, inc)
]
sgd.train(dataset=dataset)
assert all(
np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in zip(manual, model.get_params()))
def test_nesterov_momentum():
"""
Make sure that learning_rule.Momentum obtains the same parameter values as
with a hand-crafted sgd w/ momentum implementation, given a dummy model and
learning rate scaler for each parameter.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
momentum = 0.5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=Momentum(momentum, nesterov_momentum=True),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
vel = [-learning_rate * scale for scale in scales]
updates = [-learning_rate * scale + v * momentum
for scale, v in izip(scales, vel)]
manual = [param + update for param, update in izip(manual, updates)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in izip(manual, model.get_params()))
vel = [-learning_rate * scale + i * momentum
for scale, i in izip(scales, vel)]
updates = [-learning_rate * scale + v * momentum
for scale, v in izip(scales, vel)]
manual = [param + update for param, update in izip(manual, updates)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in izip(manual, model.get_params()))
def test_momentum():
"""
Make sure that learning_rule.Momentum obtains the same parameter values as
with a hand-crafted sgd w/ momentum implementation, given a dummy model and
learning rate scaler for each parameter.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1,), (9,), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
learning_rate = .001
momentum = 0.5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
learning_rule=Momentum(momentum),
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
inc = [-learning_rate * scale for scale in scales]
manual = [param + i for param, i in izip(manual, inc)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in
izip(manual, model.get_params()))
manual = [param - learning_rate * scale + i * momentum
for param, scale, i in izip(manual, scales, inc)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param in
izip(manual, model.get_params()))
def test_lr_scalers_momentum():
"""
Tests that SGD respects Model.get_lr_scalers when using
momentum.
"""
# We include a cost other than SumOfParams so that data is actually
# queried from the training set, and the expected number of updates
# are applied.
cost = SumOfCosts([SumOfParams(), (0., DummyCost())])
scales = [.01, .02, .05, 1., 5.]
shapes = [(1,), (9,), (8, 7), (6, 5, 4), (3, 2, 2, 2)]
model = DummyModel(shapes, lr_scalers=scales)
dataset = ArangeDataset(1)
learning_rate = .001
momentum = 0.5
sgd = SGD(cost=cost,
learning_rate=learning_rate,
init_momentum=momentum,
batch_size=1)
sgd.setup(model=model, dataset=dataset)
manual = [param.get_value() for param in model.get_params()]
inc = [-learning_rate * scale for param, scale in zip(manual, scales)]
manual = [param + i for param, i in zip(manual, inc)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in zip(manual, model.get_params()))
manual = [param - learning_rate * scale + i * momentum
for param, scale, i in
zip(manual, scales, inc)]
sgd.train(dataset=dataset)
assert all(np.allclose(manual_param, sgd_param.get_value())
for manual_param, sgd_param
in zip(manual, model.get_params()))
class SequenceTaggerNetwork(Model):
def __init__(self,
dataset,
w2i,
t2i,
featurizer,
edim=None,
hdims=None,
fedim=None,
max_epochs=100,
use_momentum=False,
lr=.01,
lr_lin_decay=None,
lr_scale=False,
lr_monitor_decay=False,
valid_stop=False,
reg_factors=None,
dropout=False,
dropout_params=None,
embedding_init=None,
embedded_model=None,
monitor_train=True,
plot_monitor=None,
num=False):
super(SequenceTaggerNetwork, self).__init__()
self.vocab_size = dataset.vocab_size
self.window_size = dataset.window_size
self.total_feats = dataset.total_feats
self.feat_num = dataset.feat_num
self.n_classes = dataset.n_classes
self.max_epochs = max_epochs
if edim is None:
edim = 50
if hdims is None:
hdims = [100]
if fedim is None:
fedim = 5
self.edim = edim
self.fedim = fedim
self.hdims = hdims
self.w2i = w2i
self.t2i = t2i
self.featurizer = featurizer
self._create_tagger()
A_value = numpy.random.uniform(low=-.1,
high=.1,
size=(self.n_classes + 2,
self.n_classes))
self.A = sharedX(A_value, name='A')
self.use_momentum = use_momentum
self.lr = lr
self.lr_lin_decay = lr_lin_decay
self.lr_monitor_decay = lr_monitor_decay
self.lr_scale = lr_scale
self.valid_stop = valid_stop
self.reg_factors = reg_factors
self.close_cache = {}
self.dropout_params = dropout_params
self.dropout = dropout or self.dropout_params is not None
self.hdims = hdims
self.monitor_train = monitor_train
self.num = num
self.plot_monitor = plot_monitor
if embedding_init is not None:
self.set_embedding_weights(embedding_init)
def _create_tagger(self):
self.tagger = WordTaggerNetwork(self.vocab_size, self.window_size,
self.total_feats, self.feat_num,
self.hdims, self.edim, self.fedim,
self.n_classes)
def _create_data_specs(self, dataset):
self.input_space = CompositeSpace([
dataset.data_specs[0].components[i]
for i in xrange(len(dataset.data_specs[0].components) - 1)
])
self.output_space = dataset.data_specs[0].components[-1]
self.input_source = dataset.data_specs[1][:-1]
self.target_source = dataset.data_specs[1][-1]
def __getstate__(self):
d = {}
d['vocab_size'] = self.vocab_size
d['window_size'] = self.window_size
d['feat_num'] = self.feat_num
d['total_feats'] = self.total_feats
d['n_classes'] = self.n_classes
d['input_space'] = self.input_space
d['output_space'] = self.output_space
d['input_source'] = self.input_source
d['target_source'] = self.target_source
d['A'] = self.A
d['tagger'] = self.tagger
d['w2i'] = self.w2i
d['t2i'] = self.t2i
d['featurizer'] = self.featurizer
d['max_epochs'] = self.max_epochs
d['use_momentum'] = self.use_momentum
d['lr'] = self.lr
d['lr_lin_decay'] = self.lr_lin_decay
d['lr_monitor_decay'] = self.lr_monitor_decay
d['lr_scale'] = self.lr_scale
d['valid_stop'] = self.valid_stop
d['reg_factors'] = self.reg_factors
d['dropout'] = self.dropout
d['dropout_params'] = self.dropout_params
d['monitor_train'] = self.monitor_train
d['num'] = self.num
d['plot_monitor'] = self.plot_monitor
return d
def fprop(self, data):
tagger_out = self.tagger.fprop(data)
probs = T.concatenate([self.A, tagger_out])
return probs
def dropout_fprop(self,
data,
default_input_include_prob=0.5,
input_include_probs=None,
default_input_scale=2.0,
input_scales=None,
per_example=True):
if input_scales is None:
input_scales = {'input': 1.0}
if input_include_probs is None:
input_include_probs = {'input': 1.0}
if self.dropout_params is not None:
if len(self.dropout_params) == len(self.tagger.layers) - 1:
input_include_probs['tagger_out'] = self.dropout_params[-1]
input_scales['tagger_out'] = 1.0 / self.dropout_params[-1]
for i, p in enumerate(self.dropout_params[:-1]):
input_include_probs['h{0}'.format(i)] = p
input_scales['h{0}'.format(i)] = 1.0 / p
tagger_out = self.tagger.dropout_fprop(data,
default_input_include_prob,
input_include_probs,
default_input_scale,
input_scales, per_example)
probs = T.concatenate([self.A, tagger_out])
return probs
@functools.wraps(Model.get_lr_scalers)
def get_lr_scalers(self):
if not self.lr_scale:
return {}
d = self.tagger.get_lr_scalers()
d[self.A] = 1. / self.n_classes
return d
@functools.wraps(Model.get_params)
def get_params(self):
return self.tagger.get_params() + [self.A]
def create_adjustors(self):
initial_momentum = .5
final_momentum = .99
start = 1
saturate = self.max_epochs
self.momentum_adjustor = learning_rule.MomentumAdjustor(
final_momentum, start, saturate)
self.momentum_rule = learning_rule.Momentum(initial_momentum,
nesterov_momentum=True)
if self.lr_monitor_decay:
self.learning_rate_adjustor = MonitorBasedLRAdjuster(
high_trigger=1.,
shrink_amt=0.9,
low_trigger=.95,
grow_amt=1.1,
channel_name='train_objective')
elif self.lr_lin_decay:
self.learning_rate_adjustor = LinearDecayOverEpoch(
start, saturate, self.lr_lin_decay)
def compute_used_inputs(self):
seen = {'words': set(), 'feats': set()}
for sen_w in self.dataset['train'].X1:
seen['words'] |= reduce(lambda x, y: set(x) | set(y), sen_w, set())
for sen_f in self.dataset['train'].X2:
seen['feats'] |= reduce(lambda x, y: set(x) | set(y), sen_f, set())
words = set(xrange(len(self.w2i)))
feats = set(xrange(self.total_feats))
self.notseen = {
'words': numpy.array(sorted(words - seen['words'])),
'feats': numpy.array(sorted(feats - seen['feats']))
}
def set_dataset(self, data):
self._create_data_specs(data['train'])
self.dataset = data
self.compute_used_inputs()
self.tagger.notseen = self.notseen
def create_algorithm(self, data, save_best_path=None):
self.set_dataset(data)
self.create_adjustors()
term = EpochCounter(max_epochs=self.max_epochs)
if self.valid_stop:
cost_crit = MonitorBased(channel_name='valid_objective',
prop_decrease=.0,
N=3)
term = And(criteria=[cost_crit, term])
#(layers, A_weight_decay)
coeffs = None
if self.reg_factors:
rf = self.reg_factors
lhdims = len(self.tagger.hdims)
l_inputlayer = len(self.tagger.layers[0].layers)
coeffs = ([[rf] * l_inputlayer] + ([rf] * lhdims) + [rf], rf)
cost = SeqTaggerCost(coeffs, self.dropout)
self.cost = cost
self.mbsb = MonitorBasedSaveBest(channel_name='valid_objective',
save_path=save_best_path)
mon_dataset = dict(self.dataset)
if not self.monitor_train:
del mon_dataset['train']
_learning_rule = (self.momentum_rule if self.use_momentum else None)
self.algorithm = SGD(
batch_size=1,
learning_rate=self.lr,
termination_criterion=term,
monitoring_dataset=mon_dataset,
cost=cost,
learning_rule=_learning_rule,
)
self.algorithm.setup(self, self.dataset['train'])
if self.plot_monitor:
cn = ["valid_objective", "test_objective"]
if self.monitor_train:
cn.append("train_objective")
plots = Plots(channel_names=cn, save_path=self.plot_monitor)
self.pm = PlotManager([plots], freq=1)
self.pm.setup(self, None, self.algorithm)
def train(self):
while True:
if not self.algorithm.continue_learning(self):
break
self.algorithm.train(dataset=self.dataset['train'])
self.monitor.report_epoch()
self.monitor()
self.mbsb.on_monitor(self, self.dataset['valid'], self.algorithm)
if self.use_momentum:
self.momentum_adjustor.on_monitor(self, self.dataset['valid'],
self.algorithm)
if hasattr(self, 'learning_rate_adjustor'):
self.learning_rate_adjustor.on_monitor(self,
self.dataset['valid'],
self.algorithm)
if hasattr(self, 'pm'):
self.pm.on_monitor(self, self.dataset['valid'], self.algorithm)
def prepare_tagging(self):
X = self.get_input_space().make_theano_batch(batch_size=1)
Y = self.fprop(X)
self.f = theano.function([X[0], X[1]], Y)
self.start = self.A.get_value()[0]
self.end = self.A.get_value()[1]
self.A_value = self.A.get_value()[2:]
def process_input(self, words, feats):
return self.f(words, feats)
def tag_sen(self, words, feats, debug=False, return_probs=False):
if not hasattr(self, 'f'):
self.prepare_tagging()
y = self.process_input(words, feats)
tagger_out = y[2 + self.n_classes:]
res = viterbi(self.start, self.A_value, self.end, tagger_out,
self.n_classes, return_probs)
if return_probs:
return res / res.sum(axis=1)[:, numpy.newaxis]
#return res.reshape((1, len(res)))
if debug:
return numpy.array([[e] for e in res[1]]), tagger_out
return numpy.array([[e] for e in res[1]])
def get_score(self, dataset, mode='pwp'):
self.prepare_tagging()
tagged = (self.tag_sen(w, f) for w, f in izip(dataset.X1, dataset.X2))
gold = dataset.y
good, bad = 0., 0.
if mode == 'pwp':
for t, g in izip(tagged, gold):
g = g.argmax(axis=1)
t = t.flatten()
good += sum(t == g)
bad += sum(t != g)
return [good / (good + bad)]
elif mode == 'f1':
i2t = [t for t, i in sorted(self.t2i.items(), key=lambda x: x[1])]
f1c = FScCounter(i2t, binary_input=False)
gold = map(lambda x: x.argmax(axis=1), gold)
tagged = map(lambda x: x.flatten(), tagged)
return f1c.count_score(gold, tagged)
def set_embedding_weights(self, embedding_init):
# load embedding with gensim
from gensim.models import Word2Vec
try:
m = Word2Vec.load_word2vec_format(embedding_init, binary=False)
edim = m.layer1_size
except UnicodeDecodeError:
try:
m = Word2Vec.load_word2vec_format(embedding_init, binary=True)
edim = m.layer1_size
except UnicodeDecodeError:
# not in word2vec format
m = Word2Vec.load(embedding_init)
edim = m.layer1_size
except ValueError:
# glove model
m = {}
if embedding_init.endswith('gz'):
fp = gzip.open(embedding_init)
else:
fp = open(embedding_init)
for l in fp:
le = l.split()
m[le[0].decode('utf-8')] = numpy.array(
[float(e) for e in le[1:]], dtype=theano.config.floatX)
edim = len(le) - 1
if edim != self.edim:
raise Exception("Embedding dim and edim doesn't match")
m_lower = {}
vocab = (m.vocab if hasattr(m, 'vocab') else m)
for k in vocab:
if k in ['UNKNOWN', 'PADDING']:
continue
if self.num:
m_lower[replace_numerals(k.lower())] = m[k]
else:
m_lower[k.lower()] = m[k]
# transform weight matrix with using self.w2i
params = numpy.zeros(
self.tagger.layers[0].layers[0].get_param_vector().shape,
dtype=theano.config.floatX)
e = self.edim
for w in self.w2i:
if w in m_lower:
v = m_lower[w]
i = self.w2i[w]
params[i * e:(i + 1) * e] = v
if 'UNKNOWN' in vocab:
params[-1 * e:] = vocab['UNKNOWN']
if 'PADDING' in vocab:
params[-2 * e:-1 * e] = vocab['PADDING']
self.tagger.layers[0].layers[0].set_param_vector(params)
class SequenceTaggerNetwork(Model):
def __init__(self, dataset, w2i, t2i, featurizer,
edim=None, hdims=None, fedim=None,
max_epochs=100, use_momentum=False, lr=.01, lr_lin_decay=None,
lr_scale=False, lr_monitor_decay=False,
valid_stop=False, reg_factors=None, dropout=False,
dropout_params=None, embedding_init=None,
embedded_model=None, monitor_train=True, plot_monitor=None,
num=False):
super(SequenceTaggerNetwork, self).__init__()
self.vocab_size = dataset.vocab_size
self.window_size = dataset.window_size
self.total_feats = dataset.total_feats
self.feat_num = dataset.feat_num
self.n_classes = dataset.n_classes
self.max_epochs = max_epochs
if edim is None:
edim = 50
if hdims is None:
hdims = [100]
if fedim is None:
fedim = 5
self.edim = edim
self.fedim = fedim
self.hdims = hdims
self.w2i = w2i
self.t2i = t2i
self.featurizer = featurizer
self._create_tagger()
A_value = numpy.random.uniform(low=-.1, high=.1,
size=(self.n_classes + 2,
self.n_classes))
self.A = sharedX(A_value, name='A')
self.use_momentum = use_momentum
self.lr = lr
self.lr_lin_decay = lr_lin_decay
self.lr_monitor_decay = lr_monitor_decay
self.lr_scale = lr_scale
self.valid_stop = valid_stop
self.reg_factors = reg_factors
self.close_cache = {}
self.dropout_params = dropout_params
self.dropout = dropout or self.dropout_params is not None
self.hdims = hdims
self.monitor_train = monitor_train
self.num = num
self.plot_monitor = plot_monitor
if embedding_init is not None:
self.set_embedding_weights(embedding_init)
def _create_tagger(self):
self.tagger = WordTaggerNetwork(
self.vocab_size, self.window_size, self.total_feats,
self.feat_num, self.hdims, self.edim, self.fedim, self.n_classes)
def _create_data_specs(self, dataset):
self.input_space = CompositeSpace([
dataset.data_specs[0].components[i]
for i in xrange(len(dataset.data_specs[0].components) - 1)])
self.output_space = dataset.data_specs[0].components[-1]
self.input_source = dataset.data_specs[1][:-1]
self.target_source = dataset.data_specs[1][-1]
def __getstate__(self):
d = {}
d['vocab_size'] = self.vocab_size
d['window_size'] = self.window_size
d['feat_num'] = self.feat_num
d['total_feats'] = self.total_feats
d['n_classes'] = self.n_classes
d['input_space'] = self.input_space
d['output_space'] = self.output_space
d['input_source'] = self.input_source
d['target_source'] = self.target_source
d['A'] = self.A
d['tagger'] = self.tagger
d['w2i'] = self.w2i
d['t2i'] = self.t2i
d['featurizer'] = self.featurizer
d['max_epochs'] = self.max_epochs
d['use_momentum'] = self.use_momentum
d['lr'] = self.lr
d['lr_lin_decay'] = self.lr_lin_decay
d['lr_monitor_decay'] = self.lr_monitor_decay
d['lr_scale'] = self.lr_scale
d['valid_stop'] = self.valid_stop
d['reg_factors'] = self.reg_factors
d['dropout'] = self.dropout
d['dropout_params'] = self.dropout_params
d['monitor_train'] = self.monitor_train
d['num'] = self.num
d['plot_monitor'] = self.plot_monitor
return d
def fprop(self, data):
tagger_out = self.tagger.fprop(data)
probs = T.concatenate([self.A, tagger_out])
return probs
def dropout_fprop(self, data, default_input_include_prob=0.5,
input_include_probs=None, default_input_scale=2.0,
input_scales=None, per_example=True):
if input_scales is None:
input_scales = {'input': 1.0}
if input_include_probs is None:
input_include_probs = {'input': 1.0}
if self.dropout_params is not None:
if len(self.dropout_params) == len(self.tagger.layers) - 1:
input_include_probs['tagger_out'] = self.dropout_params[-1]
input_scales['tagger_out'] = 1.0/self.dropout_params[-1]
for i, p in enumerate(self.dropout_params[:-1]):
input_include_probs['h{0}'.format(i)] = p
input_scales['h{0}'.format(i)] = 1.0/p
tagger_out = self.tagger.dropout_fprop(
data, default_input_include_prob, input_include_probs,
default_input_scale, input_scales, per_example)
probs = T.concatenate([self.A, tagger_out])
return probs
@functools.wraps(Model.get_lr_scalers)
def get_lr_scalers(self):
if not self.lr_scale:
return {}
d = self.tagger.get_lr_scalers()
d[self.A] = 1. / self.n_classes
return d
@functools.wraps(Model.get_params)
def get_params(self):
return self.tagger.get_params() + [self.A]
def create_adjustors(self):
initial_momentum = .5
final_momentum = .99
start = 1
saturate = self.max_epochs
self.momentum_adjustor = learning_rule.MomentumAdjustor(
final_momentum, start, saturate)
self.momentum_rule = learning_rule.Momentum(initial_momentum,
nesterov_momentum=True)
if self.lr_monitor_decay:
self.learning_rate_adjustor = MonitorBasedLRAdjuster(
high_trigger=1., shrink_amt=0.9,
low_trigger=.95, grow_amt=1.1, channel_name='train_objective')
elif self.lr_lin_decay:
self.learning_rate_adjustor = LinearDecayOverEpoch(
start, saturate, self.lr_lin_decay)
def compute_used_inputs(self):
seen = {'words': set(), 'feats': set()}
for sen_w in self.dataset['train'].X1:
seen['words'] |= reduce(
lambda x, y: set(x) | set(y),
sen_w, set())
for sen_f in self.dataset['train'].X2:
seen['feats'] |= reduce(
lambda x, y: set(x) | set(y),
sen_f, set())
words = set(xrange(len(self.w2i)))
feats = set(xrange(self.total_feats))
self.notseen = {
'words': numpy.array(sorted(words - seen['words'])),
'feats': numpy.array(sorted(feats - seen['feats']))
}
def set_dataset(self, data):
self._create_data_specs(data['train'])
self.dataset = data
self.compute_used_inputs()
self.tagger.notseen = self.notseen
def create_algorithm(self, data, save_best_path=None):
self.set_dataset(data)
self.create_adjustors()
term = EpochCounter(max_epochs=self.max_epochs)
if self.valid_stop:
cost_crit = MonitorBased(channel_name='valid_objective',
prop_decrease=.0, N=3)
term = And(criteria=[cost_crit, term])
#(layers, A_weight_decay)
coeffs = None
if self.reg_factors:
rf = self.reg_factors
lhdims = len(self.tagger.hdims)
l_inputlayer = len(self.tagger.layers[0].layers)
coeffs = ([[rf] * l_inputlayer] + ([rf] * lhdims) + [rf], rf)
cost = SeqTaggerCost(coeffs, self.dropout)
self.cost = cost
self.mbsb = MonitorBasedSaveBest(channel_name='valid_objective',
save_path=save_best_path)
mon_dataset = dict(self.dataset)
if not self.monitor_train:
del mon_dataset['train']
_learning_rule = (self.momentum_rule if self.use_momentum else None)
self.algorithm = SGD(batch_size=1, learning_rate=self.lr,
termination_criterion=term,
monitoring_dataset=mon_dataset,
cost=cost,
learning_rule=_learning_rule,
)
self.algorithm.setup(self, self.dataset['train'])
if self.plot_monitor:
cn = ["valid_objective", "test_objective"]
if self.monitor_train:
cn.append("train_objective")
plots = Plots(channel_names=cn, save_path=self.plot_monitor)
self.pm = PlotManager([plots], freq=1)
self.pm.setup(self, None, self.algorithm)
def train(self):
while True:
if not self.algorithm.continue_learning(self):
break
self.algorithm.train(dataset=self.dataset['train'])
self.monitor.report_epoch()
self.monitor()
self.mbsb.on_monitor(self, self.dataset['valid'], self.algorithm)
if self.use_momentum:
self.momentum_adjustor.on_monitor(self, self.dataset['valid'],
self.algorithm)
if hasattr(self, 'learning_rate_adjustor'):
self.learning_rate_adjustor.on_monitor(
self, self.dataset['valid'], self.algorithm)
if hasattr(self, 'pm'):
self.pm.on_monitor(
self, self.dataset['valid'], self.algorithm)
def prepare_tagging(self):
X = self.get_input_space().make_theano_batch(batch_size=1)
Y = self.fprop(X)
self.f = theano.function([X[0], X[1]], Y)
self.start = self.A.get_value()[0]
self.end = self.A.get_value()[1]
self.A_value = self.A.get_value()[2:]
def process_input(self, words, feats):
return self.f(words, feats)
def tag_sen(self, words, feats, debug=False, return_probs=False):
if not hasattr(self, 'f'):
self.prepare_tagging()
y = self.process_input(words, feats)
tagger_out = y[2 + self.n_classes:]
res = viterbi(self.start, self.A_value, self.end, tagger_out,
self.n_classes, return_probs)
if return_probs:
return res / res.sum(axis=1)[:,numpy.newaxis]
#return res.reshape((1, len(res)))
if debug:
return numpy.array([[e] for e in res[1]]), tagger_out
return numpy.array([[e] for e in res[1]])
def get_score(self, dataset, mode='pwp'):
self.prepare_tagging()
tagged = (self.tag_sen(w, f) for w, f in
izip(dataset.X1, dataset.X2))
gold = dataset.y
good, bad = 0., 0.
if mode == 'pwp':
for t, g in izip(tagged, gold):
g = g.argmax(axis=1)
t = t.flatten()
good += sum(t == g)
bad += sum(t != g)
return [good / (good + bad)]
elif mode == 'f1':
i2t = [t for t, i in sorted(self.t2i.items(), key=lambda x: x[1])]
f1c = FScCounter(i2t, binary_input=False)
gold = map(lambda x:x.argmax(axis=1), gold)
tagged = map(lambda x:x.flatten(), tagged)
return f1c.count_score(gold, tagged)
def set_embedding_weights(self, embedding_init):
# load embedding with gensim
from gensim.models import Word2Vec
try:
m = Word2Vec.load_word2vec_format(embedding_init, binary=False)
edim = m.layer1_size
except UnicodeDecodeError:
try:
m = Word2Vec.load_word2vec_format(embedding_init, binary=True)
edim = m.layer1_size
except UnicodeDecodeError:
# not in word2vec format
m = Word2Vec.load(embedding_init)
edim = m.layer1_size
except ValueError:
# glove model
m = {}
if embedding_init.endswith('gz'):
fp = gzip.open(embedding_init)
else:
fp = open(embedding_init)
for l in fp:
le = l.split()
m[le[0].decode('utf-8')] = numpy.array(
[float(e) for e in le[1:]], dtype=theano.config.floatX)
edim = len(le) - 1
if edim != self.edim:
raise Exception("Embedding dim and edim doesn't match")
m_lower = {}
vocab = (m.vocab if hasattr(m, 'vocab') else m)
for k in vocab:
if k in ['UNKNOWN', 'PADDING']:
continue
if self.num:
m_lower[replace_numerals(k.lower())] = m[k]
else:
m_lower[k.lower()] = m[k]
# transform weight matrix with using self.w2i
params = numpy.zeros(
self.tagger.layers[0].layers[0].get_param_vector().shape, dtype=theano.config.floatX)
e = self.edim
for w in self.w2i:
if w in m_lower:
v = m_lower[w]
i = self.w2i[w]
params[i*e:(i+1)*e] = v
if 'UNKNOWN' in vocab:
params[-1*e:] = vocab['UNKNOWN']
if 'PADDING' in vocab:
params[-2*e:-1*e] = vocab['PADDING']
self.tagger.layers[0].layers[0].set_param_vector(params)
istdev = .05
)
layers = [layer0, layer1, layer3]
#layers = [layer0, layer2, layer3]
ann = MLP(layers, input_space=ishape)
t_algo = SGD(learning_rate = 1e-1,
batch_size = 100,
batches_per_iter = 1,
termination_criterion=EpochCounter(2)
)
ds = DataPylearn2([train_set_x,train_set_y],[48,48,1],7)
t_algo.setup(ann, ds)
while True:
t_algo.train(dataset=ds)
ann.monitor.report_epoch()
ann.monitor()
if not t_algo.continue_learning(ann):
break
# test: https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/icml_2013_wrepl/emotions/make_submission.py
ds2 = DataPylearn2([test_set_x,test_set_y],[48,48,1],-1)
m = ds2.X.shape[0]
batch_size = 100
extra = (batch_size - m % batch_size) % batch_size
assert (m + extra) % batch_size == 0
if extra > 0:
ds2.X = np.concatenate((ds2.X, np.zeros((extra, ds2.X.shape[1]),
dtype=ds2.X.dtype)), axis=0)
assert ds2.X.shape[0] % batch_size == 0
import theano
import numpy as np
n = 200
p = 2
X = np.random.normal(0, 1, (n, p))
y = X[:,0]* X[:, 1] + np.random.normal(0, .1, n)
y.shape = (n, 1)
ds = DenseDesignMatrix(X=X, y=y)
hidden_layer = Sigmoid(layer_name='hidden', dim=10, irange=.1, init_bias=1.)
output_layer = Linear(dim=1, layer_name='y', irange=.1)
trainer = SGD(learning_rate=.05, batch_size=10,
termination_criterion=EpochCounter(200))
layers = [hidden_layer, output_layer]
ann = MLP(layers, nvis=2)
trainer.setup(ann, ds)
while True:
trainer.train(dataset=ds)
ann.monitor.report_epoch()
ann.monitor()
if not trainer.continue_learning(ann):
break
inputs = X
y_est = ann.fprop(theano.shared(inputs, name='inputs')).eval()
print(y_est.shape)
