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_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_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 get_costs(self, cost_array):
costs = []
for cost_id in cost_array:
costs.extend(self.get_cost(cost_id))
if len(costs) > 1:
cost = SumOfCosts(costs)
else:
cost = costs[0]
return cost
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 model2():
#pdb.set_trace()
# train set X has dim (60,000, 784), y has dim (60,000, 10)
train_set = MNIST(which_set='train', one_hot=True)
# test set X has dim (10,000, 784), y has dim (10,000, 10)
test_set = MNIST(which_set='test', one_hot=True)
# =====<Create the MLP Model>=====
h1_layer = RectifiedLinear(layer_name='h1', dim=1000, irange=0.5)
#print h1_layer.get_params()
h2_layer = RectifiedLinear(layer_name='h2',
dim=1000,
sparse_init=15,
max_col_norm=1)
y_layer = Softmax(layer_name='y',
n_classes=train_set.y.shape[1],
irange=0.5)
mlp = MLP(batch_size=100,
input_space=VectorSpace(dim=train_set.X.shape[1]),
layers=[h1_layer, h2_layer, y_layer])
# =====<Create the SGD algorithm>=====
sgd = SGD(batch_size=100,
init_momentum=0.1,
learning_rate=0.01,
monitoring_dataset={
'valid': train_set,
'test': test_set
},
cost=SumOfCosts(costs=[
MethodCost('cost_from_X'),
WeightDecay(coeffs=[0.00005, 0.00005, 0.00005])
]),
termination_criterion=MonitorBased(
channel_name='valid_y_misclass', prop_decrease=0.0001, N=5))
#sgd.setup(model=mlp, dataset=train_set)
# =====<Extensions>=====
ext = [MomentumAdjustor(start=1, saturate=10, final_momentum=0.99)]
# =====<Create Training Object>=====
save_path = './mlp_model2.pkl'
train_obj = Train(dataset=train_set,
model=mlp,
algorithm=sgd,
extensions=ext,
save_path=save_path,
save_freq=0)
#train_obj.setup_extensions()
train_obj.main_loop()
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 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 prepare_adagrad_test(dataset_type='arange', model_type='random'):
"""
Factor out common code for AdaGrad tests.
Parameters
----------
dataset_type : string, optional
Can use either `arange` to use an ArangeDataset instance or
`zeros` to create an all-zeros DenseDesignMatrix.
model_type : string, optional
How to initialize the model; `random` will initialize parameters
to random values, `zeros` to zero.
"""
# 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, init_type=model_type)
if dataset_type == 'arange':
dataset = ArangeDataset(1)
elif dataset_type == 'zeros':
X = np.zeros((1, 1))
X[:, 0] = np.arange(1)
dataset = DenseDesignMatrix(X)
else:
raise ValueError('Unknown value for dataset_type: %s', dataset_type)
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)
return (cost, model, dataset, sgd, state)
def test_correctness():
"""
Test that the cost function works with float64
"""
x_train, y_train, x_valid, y_valid = create_dataset()
trainset = DenseDesignMatrix(X=np.array(x_train), y=y_train)
validset = DenseDesignMatrix(X=np.array(x_valid), y=y_valid)
n_inputs = trainset.X.shape[1]
n_outputs = 1
n_hidden = 10
hidden_istdev = 4 * (6 / float(n_inputs + n_hidden)) ** 0.5
output_istdev = 4 * (6 / float(n_hidden + n_outputs)) ** 0.5
model = MLP(layers=[Sigmoid(dim=n_hidden, layer_name='hidden',
istdev=hidden_istdev),
Sigmoid(dim=n_outputs, layer_name='output',
istdev=output_istdev)],
nvis=n_inputs, seed=[2013, 9, 16])
termination_criterion = And([EpochCounter(max_epochs=1),
MonitorBased(prop_decrease=1e-7,
N=2)])
cost = SumOfCosts([(0.99, Default()),
(0.01, L1WeightDecay({}))])
algo = SGD(1e-1,
update_callbacks=[ExponentialDecay(decay_factor=1.00001,
min_lr=1e-10)],
cost=cost,
monitoring_dataset=validset,
termination_criterion=termination_criterion,
monitor_iteration_mode='even_shuffled_sequential',
batch_size=2)
train = Train(model=model, dataset=trainset, algorithm=algo)
train.main_loop()
def test_fixed_vars():
"""
A very basic test of the the fixed vars interface.
Checks that the costs' expr and get_gradients methods
are called with the right parameters and that the updates
functions are called the right number of times.
"""
rng = np.random.RandomState([2012, 11, 27, 9])
batch_size = 5
updates_per_batch = 4
train_batches = 3
num_features = 2
# Synthesize dataset with a linear decision boundary
w = rng.randn(num_features)
def make_dataset(num_batches):
m = num_batches * batch_size
X = rng.randn(m, num_features)
y = rng.randn(m, num_features)
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
return rval
train = make_dataset(train_batches)
model = SoftmaxModel(num_features)
unsup_counter = shared(0)
grad_counter = shared(0)
called = [False, False, False, False]
class UnsupervisedCostWithFixedVars(Cost):
def expr(self, model, data, unsup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
X = data
assert unsup_aux_var is unsup_counter
called[0] = True
return (model.P * X).sum()
def get_gradients(self, model, data, unsup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
assert unsup_aux_var is unsup_counter
called[1] = True
gradients, updates = Cost.get_gradients(
self, model, data, unsup_aux_var=unsup_aux_var)
updates[grad_counter] = grad_counter + 1
return gradients, updates
def get_fixed_var_descr(self, model, data, **kwargs):
data_specs = self.get_data_specs(model)
data_specs[0].validate(data)
rval = FixedVarDescr()
rval.fixed_vars = {'unsup_aux_var': unsup_counter}
rval.data_specs = data_specs
# The input to function should be a flat, non-redundent tuple
mapping = DataSpecsMapping(data_specs)
data_tuple = mapping.flatten(data, return_tuple=True)
theano_func = function(data_tuple,
updates=[(unsup_counter, unsup_counter + 1)
])
# the on_load_batch function will take numerical data formatted
# as rval.data_specs, so we have to flatten it inside the
# returned function too.
# Using default argument binds the variables used in the lambda
# function to the value they have when the lambda is defined.
on_load = (lambda batch, mapping=mapping, theano_func=theano_func:
theano_func(*mapping.flatten(batch, return_tuple=True)))
rval.on_load_batch = [on_load]
return rval
def get_data_specs(self, model):
return (model.get_input_space(), model.get_input_source())
sup_counter = shared(0)
class SupervisedCostWithFixedVars(Cost):
supervised = True
def expr(self, model, data, sup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
X, Y = data
assert sup_aux_var is sup_counter
called[2] = True
return (model.P * X * Y).sum()
def get_gradients(self, model, data, sup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
assert sup_aux_var is sup_counter
called[3] = True
return super(SupervisedCostWithFixedVars,
self).get_gradients(model=model,
data=data,
sup_aux_var=sup_aux_var)
def get_fixed_var_descr(self, model, data):
data_specs = self.get_data_specs(model)
data_specs[0].validate(data)
rval = FixedVarDescr()
rval.fixed_vars = {'sup_aux_var': sup_counter}
rval.data_specs = data_specs
# data has to be flattened into a tuple before being passed
# to `function`.
mapping = DataSpecsMapping(data_specs)
flat_data = mapping.flatten(data, return_tuple=True)
theano_func = function(flat_data,
updates=[(sup_counter, sup_counter + 1)])
# the on_load_batch function will take numerical data formatted
# as rval.data_specs, so we have to flatten it inside the
# returned function too.
# Using default argument binds the variables used in the lambda
# function to the value they have when the lambda is defined.
on_load = (lambda batch, mapping=mapping, theano_func=theano_func:
theano_func(*mapping.flatten(batch, return_tuple=True)))
rval.on_load_batch = [on_load]
return rval
def get_data_specs(self, model):
space = CompositeSpace(
(model.get_input_space(), model.get_output_space()))
source = (model.get_input_source(), model.get_target_source())
return (space, source)
cost = SumOfCosts(
costs=[UnsupervisedCostWithFixedVars(),
SupervisedCostWithFixedVars()])
algorithm = BGD(cost=cost,
batch_size=batch_size,
conjugate=1,
line_search_mode='exhaustive',
updates_per_batch=updates_per_batch)
algorithm.setup(model=model, dataset=train)
# Make sure all the right methods were used to compute the updates
assert all(called)
algorithm.train(dataset=train)
# Make sure the load_batch callbacks were called the right amount of times
assert unsup_counter.get_value() == train_batches
assert sup_counter.get_value() == train_batches
# Make sure the gradient updates were run the right amount of times
assert grad_counter.get_value() == train_batches * updates_per_batch
def test_fixed_vars():
"""
A very basic test of the the fixed vars interface.
Checks that the costs' expr and get_gradients methods
are called with the right parameters and that the updates
functions are called the right number of times.
"""
"""
Notes: this test is fairly messy. PL made some change to how
FixedVarDescr worked. FixedVarDescr got an added data_specs
field. But BGD itself was never changed to obey this data_specs.
Somehow these tests passed regardless. It looks like PL just built
a lot of machinery into the test itself to make the individual
callbacks reformat data internally. This mechanism required the
data_specs field to be present. Weirdly, the theano functions
never actually used any of the data, so their data_specs should
have just been NullSpace anyway. IG deleted a lot of this useless
code from these tests but there is still a lot of weird stuff here
that he has not attempted to clean up.
"""
rng = np.random.RandomState([2012, 11, 27, 9])
batch_size = 5
updates_per_batch = 4
train_batches = 3
num_features = 2
# Synthesize dataset with a linear decision boundary
w = rng.randn(num_features)
def make_dataset(num_batches):
m = num_batches * batch_size
X = rng.randn(m, num_features)
y = rng.randn(m, num_features)
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
return rval
train = make_dataset(train_batches)
model = SoftmaxModel(num_features)
unsup_counter = shared(0)
grad_counter = shared(0)
called = [False, False, False, False]
class UnsupervisedCostWithFixedVars(Cost):
def expr(self, model, data, unsup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
X = data
assert unsup_aux_var is unsup_counter
called[0] = True
return (model.P * X).sum()
def get_gradients(self, model, data, unsup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
assert unsup_aux_var is unsup_counter
called[1] = True
gradients, updates = Cost.get_gradients(
self, model, data, unsup_aux_var=unsup_aux_var)
updates[grad_counter] = grad_counter + 1
return gradients, updates
def get_fixed_var_descr(self, model, data, **kwargs):
data_specs = self.get_data_specs(model)
data_specs[0].validate(data)
rval = FixedVarDescr()
rval.fixed_vars = {'unsup_aux_var': unsup_counter}
# The input to function should be a flat, non-redundent tuple
mapping = DataSpecsMapping(data_specs)
data_tuple = mapping.flatten(data, return_tuple=True)
theano_func = function([],
updates=[(unsup_counter, unsup_counter + 1)
])
def on_load(batch, mapping=mapping, theano_func=theano_func):
return theano_func()
rval.on_load_batch = [on_load]
return rval
def get_data_specs(self, model):
return (model.get_input_space(), model.get_input_source())
sup_counter = shared(0)
class SupervisedCostWithFixedVars(Cost):
supervised = True
def expr(self, model, data, sup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
X, Y = data
assert sup_aux_var is sup_counter
called[2] = True
return (model.P * X * Y).sum()
def get_gradients(self, model, data, sup_aux_var=None, **kwargs):
self.get_data_specs(model)[0].validate(data)
assert sup_aux_var is sup_counter
called[3] = True
return super(SupervisedCostWithFixedVars,
self).get_gradients(model=model,
data=data,
sup_aux_var=sup_aux_var)
def get_fixed_var_descr(self, model, data):
data_specs = self.get_data_specs(model)
data_specs[0].validate(data)
rval = FixedVarDescr()
rval.fixed_vars = {'sup_aux_var': sup_counter}
theano_func = function([],
updates=[(sup_counter, sup_counter + 1)])
def on_load(data):
theano_func()
rval.on_load_batch = [on_load]
return rval
def get_data_specs(self, model):
space = CompositeSpace(
(model.get_input_space(), model.get_output_space()))
source = (model.get_input_source(), model.get_target_source())
return (space, source)
cost = SumOfCosts(
costs=[UnsupervisedCostWithFixedVars(),
SupervisedCostWithFixedVars()])
algorithm = BGD(cost=cost,
batch_size=batch_size,
conjugate=1,
line_search_mode='exhaustive',
updates_per_batch=updates_per_batch)
algorithm.setup(model=model, dataset=train)
# Make sure all the right methods were used to compute the updates
assert all(called)
algorithm.train(dataset=train)
# Make sure the load_batch callbacks were called the right amount of times
assert unsup_counter.get_value() == train_batches
assert sup_counter.get_value() == train_batches
# Make sure the gradient updates were run the right amount of times
assert grad_counter.get_value() == train_batches * updates_per_batch
def test_fixed_vars():
"""
A very basic test of the the fixed vars interface.
Checks that the costs' __call__ and get_gradients methods
are called with the right parameters and that the updates
functions are called the right number of times.
"""
rng = np.random.RandomState([2012, 11, 27, 9])
batch_size = 5
updates_per_batch = 4
train_batches = 3
num_features = 2
# Synthesize dataset with a linear decision boundary
w = rng.randn(num_features)
def make_dataset(num_batches):
m = num_batches * batch_size
X = rng.randn(m, num_features)
y = rng.randn(m, num_features)
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
return rval
train = make_dataset(train_batches)
model = SoftmaxModel(num_features)
unsup_counter = shared(0)
grad_counter = shared(0)
called = [False, False, False, False]
class UnsupervisedCostWithFixedVars(Cost):
def __call__(self, model, X, Y=None, unsup_aux_var=None, **kwargs):
assert unsup_aux_var is unsup_counter
called[0] = True
return (model.P * X).sum()
def get_gradients(self,
model,
X,
Y=None,
unsup_aux_var=None,
**kwargs):
assert unsup_aux_var is unsup_counter
called[1] = True
gradients, updates = Cost.get_gradients(
self, model, X, Y, unsup_aux_var=unsup_aux_var)
updates[grad_counter] = grad_counter + 1
return gradients, updates
def get_fixed_var_descr(self, model, X, Y, **kwargs):
rval = FixedVarDescr()
rval.fixed_vars = {'unsup_aux_var': unsup_counter}
Y = T.matrix()
theano_func = function([X, Y],
updates=[(unsup_counter, unsup_counter + 1)
])
rval.on_load_batch = [theano_func]
return rval
sup_counter = shared(0)
class SupervisedCostWithFixedVars(Cost):
supervised = True
def __call__(self, model, X, Y=None, sup_aux_var=None, **kwargs):
assert sup_aux_var is sup_counter
called[2] = True
return (model.P * X * Y).sum()
def get_gradients(self, model, X, Y=None, sup_aux_var=None, **kwargs):
assert sup_aux_var is sup_counter
called[3] = True
return super(SupervisedCostWithFixedVars,
self).get_gradients(model=model,
X=X,
Y=Y,
sup_aux_var=sup_aux_var)
def get_fixed_var_descr(self, model, X, Y=None):
rval = FixedVarDescr()
rval.fixed_vars = {'sup_aux_var': sup_counter}
rval.on_load_batch = [
function([X, Y], updates=[(sup_counter, sup_counter + 1)])
]
return rval
cost = SumOfCosts(
costs=[UnsupervisedCostWithFixedVars(),
SupervisedCostWithFixedVars()])
algorithm = BGD(cost=cost,
batch_size=batch_size,
conjugate=1,
line_search_mode='exhaustive',
updates_per_batch=updates_per_batch)
algorithm.setup(model=model, dataset=train)
# Make sure all the right methods were used to compute the updates
assert all(called)
algorithm.train(dataset=train)
# Make sure the load_batch callbacks were called the right amount of times
assert unsup_counter.get_value() == train_batches
assert sup_counter.get_value() == train_batches
# Make sure the gradient updates were run the right amount of times
assert grad_counter.get_value() == train_batches * updates_per_batch
def supervisedLayerwisePRL(trainset, testset):
'''
The supervised layerwise training as used in the PRL Paper.
Input
------
trainset : A path to an hdf5 file created through h5py.
testset : A path to an hdf5 file created through h5py.
'''
batch_size = 100
# Both train and test h5py files are expected to have a 'topo_view' and 'y'
# datasets side them corresponding to the 'b01c' data format as used in pylearn2
# and 'y' equivalent to the one hot encoded labels
trn = HDF5Dataset(filename=trainset,
topo_view='topo_view',
y='y',
load_all=False)
tst = HDF5Dataset(filename=testset,
topo_view='topo_view',
y='y',
load_all=False)
'''
The 1st Convolution and Pooling Layers are added below.
'''
h1 = mlp.ConvRectifiedLinear(layer_name='h1',
output_channels=64,
irange=0.05,
kernel_shape=[4, 4],
pool_shape=[4, 4],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
fc = mlp.RectifiedLinear(layer_name='fc', dim=1500, irange=0.05)
output = mlp.Softmax(layer_name='y',
n_classes=171,
irange=.005,
max_col_norm=1.9365)
layers = [h1, fc, output]
mdl = mlp.MLP(layers,
input_space=Conv2DSpace(shape=(70, 70), num_channels=1))
trainer = sgd.SGD(
learning_rate=0.002,
batch_size=batch_size,
learning_rule=learning_rule.RMSProp(),
cost=SumOfCosts(
costs=[Default(),
WeightDecay(coeffs=[0.0005, 0.0005, 0.0005])]),
train_iteration_mode='shuffled_sequential',
monitor_iteration_mode='sequential',
termination_criterion=EpochCounter(max_epochs=15),
monitoring_dataset={
'test': tst,
'valid': vld
})
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_y_misclass',
save_path='./Saved Models/conv_supervised_layerwise_best1.pkl')
decay = sgd.LinearDecayOverEpoch(start=8, saturate=15, decay_factor=0.1)
experiment = Train(
dataset=trn,
model=mdl,
algorithm=trainer,
extensions=[watcher, decay],
)
experiment.main_loop()
del mdl
mdl = serial.load('./Saved Models/conv_supervised_layerwise_best1.pkl')
mdl = push_monitor(mdl, 'k')
'''
The 2nd Convolution and Pooling Layers are added below.
'''
h2 = mlp.ConvRectifiedLinear(layer_name='h2',
output_channels=64,
irange=0.05,
kernel_shape=[4, 4],
pool_shape=[4, 4],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
fc = mlp.RectifiedLinear(layer_name='fc', dim=1500, irange=0.05)
output = mlp.Softmax(layer_name='y',
n_classes=171,
irange=.005,
max_col_norm=1.9365)
del mdl.layers[-1]
mdl.layer_names.remove('y')
del mdl.layers[-1]
mdl.layer_names.remove('fc')
mdl.add_layers([h2, fc, output])
trainer = sgd.SGD(learning_rate=0.002,
batch_size=batch_size,
learning_rule=learning_rule.RMSProp(),
cost=SumOfCosts(costs=[
Default(),
WeightDecay(coeffs=[0.0005, 0.0005, 0.0005, 0.0005])
]),
train_iteration_mode='shuffled_sequential',
monitor_iteration_mode='sequential',
termination_criterion=EpochCounter(max_epochs=15),
monitoring_dataset={
'test': tst,
'valid': vld
})
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_y_misclass',
save_path='./Saved Models/conv_supervised_layerwise_best2.pkl')
decay = sgd.LinearDecayOverEpoch(start=8, saturate=15, decay_factor=0.1)
experiment = Train(
dataset=trn,
model=mdl,
algorithm=trainer,
extensions=[watcher, decay],
)
experiment.main_loop()
del mdl
mdl = serial.load('./Saved Models/conv_supervised_layerwise_best2.pkl')
mdl = push_monitor(mdl, 'l')
'''
The 3rd Convolution and Pooling Layers are added below.
'''
h3 = mlp.ConvRectifiedLinear(layer_name='h2',
output_channels=64,
irange=0.05,
kernel_shape=[4, 4],
pool_shape=[4, 4],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
fc = mlp.RectifiedLinear(layer_name='h3', dim=1500, irange=0.05)
output = mlp.Softmax(layer_name='y',
n_classes=10,
irange=.005,
max_col_norm=1.9365)
del mdl.layers[-1]
mdl.layer_names.remove('y')
del mdl.layers[-1]
mdl.layer_names.remove('fc')
mdl.add_layers([h3, output])
trainer = sgd.SGD(
learning_rate=.002,
batch_size=batch_size,
learning_rule=learning_rule.RMSProp(),
cost=SumOfCosts(costs=[
Default(),
WeightDecay(coeffs=[0.0005, 0.0005, 0.0005, 0.0005, 0.0005])
]),
train_iteration_mode='shuffled_sequential',
monitor_iteration_mode='sequential',
termination_criterion=EpochCounter(max_epochs=15),
monitoring_dataset={
'test': tst,
'valid': vld
})
watcher = best_params.MonitorBasedSaveBest(
channel_name='valid_y_misclass',
save_path='./Saved Models/conv_supervised_layerwise_best3.pkl')
decay = sgd.LinearDecayOverEpoch(start=8, saturate=15, decay_factor=0.1)
experiment = Train(
dataset=trn,
model=mdl,
algorithm=trainer,
extensions=[watcher, decay],
)
experiment.main_loop()
y = Softmax(n_classes = 2,
layer_name = "y",
irange = 0.1)
inputSpace = Conv2DSpace(shape = [cropSize,cropSize],
num_channels = 3)
model = MLP(layers = [h0, h1, y],
batch_size = batchSize,
input_space = inputSpace)
algorithm = SGD(learning_rate = 1E-3,
cost = SumOfCosts([
MethodCost("cost_from_X"),
Dropout(default_input_include_prob = 0.25,
default_input_scale = 1.3333)
]),
batch_size = batchSize,
monitoring_batch_size = batchSize,
monitoring_dataset = {'train': train,
'valid':valid},
monitor_iteration_mode = "even_batchwise_shuffled_sequential",
termination_criterion = EpochCounter(max_epochs = 200),
learning_rule = Momentum(init_momentum = 0.0),
train_iteration_mode = "even_batchwise_shuffled_sequential")
train = Train(dataset = train,
model = model,
algorithm = algorithm,
save_path = "ConvNet8.pkl",