def __init__(self,
runner,
model_params,
resume=False,
resume_data=None,
s3_data=None,
**kwargs):
dataset = create_dense_design_matrix(x=runner.dp.train_set_x)
if resume:
model, model_params = self.resume_model(model_params, resume_data)
else:
model = self.new_model(model_params, dataset=dataset)
termination_criterion = MaxEpochNumber(model_params['maxnum_iter'])
algorithm = SGD(learning_rate=model_params['learning_rate']['init'],
monitoring_dataset=dataset,
cost=MeanSquaredReconstructionError(),
termination_criterion=termination_criterion,
batch_size=model_params['batch_size'])
ext = AutoEncoderStatReporter(runner,
resume=resume,
resume_data=resume_data,
save_freq=model_params['save_freq'])
self.train_obj = Train(dataset=dataset,
model=model,
algorithm=algorithm,
extensions=[ext])
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_multiple_inputs():
"""
Create a VectorSpacesDataset with two inputs (features0 and features1)
and train an MLP which takes both inputs for 1 epoch.
"""
mlp = MLP(layers=[
FlattenerLayer(
CompositeLayer('composite',
[Linear(10, 'h0', 0.1),
Linear(10, 'h1', 0.1)], {
0: [1],
1: [0]
})),
Softmax(5, 'softmax', 0.1)
],
input_space=CompositeSpace([VectorSpace(15),
VectorSpace(20)]),
input_source=('features0', 'features1'))
dataset = VectorSpacesDataset(
(np.random.rand(20, 20).astype(theano.config.floatX),
np.random.rand(20, 15).astype(theano.config.floatX),
np.random.rand(20, 5).astype(theano.config.floatX)),
(CompositeSpace(
[VectorSpace(20), VectorSpace(15),
VectorSpace(5)]), ('features1', 'features0', 'targets')))
train = Train(dataset, mlp, SGD(0.1, batch_size=5))
train.algorithm.termination_criterion = EpochCounter(1)
train.main_loop()
def get_layer_trainer_sgd_autoencoder(layer,
trainset,
batch_size=10,
learning_rate=0.1,
max_epochs=100,
name=''):
# configs on sgd
train_algo = SGD(
learning_rate=learning_rate,
# learning_rule = AdaDelta(),
learning_rule=Momentum(init_momentum=0.5),
cost=MeanSquaredReconstructionError(),
batch_size=batch_size,
monitoring_dataset=trainset,
termination_criterion=EpochCounter(max_epochs=max_epochs),
update_callbacks=None)
log_callback = LoggingCallback(name)
return Train(model=layer,
algorithm=train_algo,
extensions=[
log_callback,
OneOverEpoch(start=1, half_life=5),
MomentumAdjustor(final_momentum=0.7,
start=10,
saturate=100)
],
dataset=trainset)
def train_with_monitoring_datasets(train_dataset,
monitoring_datasets,
model_force_batch_size,
train_iteration_mode,
monitor_iteration_mode):
model = SoftmaxModel(dim)
if model_force_batch_size:
model.force_batch_size = model_force_batch_size
cost = DummyCost()
algorithm = SGD(learning_rate, cost,
batch_size=batch_size,
train_iteration_mode=train_iteration_mode,
monitor_iteration_mode=monitor_iteration_mode,
monitoring_dataset=monitoring_datasets,
termination_criterion=EpochCounter(2))
train = Train(train_dataset,
model,
algorithm,
save_path=None,
save_freq=0,
extensions=None)
train.main_loop()
def test_pylearn2_trainin():
# Construct the model
mlp = MLP(activations=[Sigmoid(), Sigmoid()],
dims=[784, 100, 784],
weights_init=IsotropicGaussian(),
biases_init=Constant(0.01))
mlp.initialize()
cost = SquaredError()
block_cost = BlocksCost(cost)
block_model = BlocksModel(mlp, (VectorSpace(dim=784), 'features'))
# Load the data
rng = numpy.random.RandomState(14)
train_dataset = random_dense_design_matrix(rng, 1024, 784, 10)
valid_dataset = random_dense_design_matrix(rng, 1024, 784, 10)
# Silence Pylearn2's logger
logger = logging.getLogger(pylearn2.__name__)
logger.setLevel(logging.ERROR)
# Training algorithm
sgd = SGD(learning_rate=0.01,
cost=block_cost,
batch_size=128,
monitoring_dataset=valid_dataset)
train = Train(train_dataset, block_model, algorithm=sgd)
train.main_loop(time_budget=3)
def test_train_ae():
GC = GaussianCorruptor
gsn = GSN.new(layer_sizes=[ds.X.shape[1], 1000],
activation_funcs=["sigmoid", "tanh"],
pre_corruptors=[None, GC(1.0)],
post_corruptors=[SaltPepperCorruptor(0.5),
GC(1.0)],
layer_samplers=[BinomialSampler(), None],
tied=False)
# average MBCE over example rather than sum it
_mbce = MeanBinaryCrossEntropy()
reconstruction_cost = lambda a, b: _mbce.cost(a, b) / ds.X.shape[1]
c = GSNCost([(0, 1.0, reconstruction_cost)], walkback=WALKBACK)
alg = SGD(LEARNING_RATE,
init_momentum=MOMENTUM,
cost=c,
termination_criterion=EpochCounter(MAX_EPOCHS),
batches_per_iter=BATCHES_PER_EPOCH,
batch_size=BATCH_SIZE,
monitoring_dataset=ds,
monitoring_batches=10)
trainer = Train(ds,
gsn,
algorithm=alg,
save_path="gsn_ae_example.pkl",
save_freq=5)
trainer.main_loop()
print "done training"
def test_execution_order():
# ensure save is called directly after monitoring by checking
# parameter values in `on_monitor` and `on_save`.
model = MLP(layers=[Softmax(layer_name='y', n_classes=2, irange=0.)],
nvis=3)
dataset = DenseDesignMatrix(X=np.random.normal(size=(6, 3)),
y=np.random.normal(size=(6, 2)))
epoch_counter = EpochCounter(max_epochs=1)
algorithm = SGD(batch_size=2,
learning_rate=0.1,
termination_criterion=epoch_counter)
extension = ParamMonitor()
train = Train(dataset=dataset,
model=model,
algorithm=algorithm,
extensions=[extension],
save_freq=1,
save_path="save.pkl")
# mock save
train.save = MethodType(only_run_extensions, train)
train.main_loop()
def get_ae_pretrainer(layer, data, batch_size, epochs=30):
init_lr = 0.05
train_algo = SGD(
batch_size=batch_size,
learning_rate=init_lr,
learning_rule=Momentum(init_momentum=0.5),
monitoring_batches=batch_size,
monitoring_dataset=data,
# for ContractiveAutoencoder:
# cost=cost.SumOfCosts(costs=[[1., MeanSquaredReconstructionError()],
# [0.5, cost.MethodCost(method='contraction_penalty')]]),
# for HigherOrderContractiveAutoencoder:
# cost=cost.SumOfCosts(costs=[[1., MeanSquaredReconstructionError()],
# [0.5, cost.MethodCost(method='contraction_penalty')],
# [0.5, cost.MethodCost(method='higher_order_penalty')]]),
# for DenoisingAutoencoder:
cost=MeanSquaredReconstructionError(),
termination_criterion=EpochCounter(epochs))
return Train(model=layer,
algorithm=train_algo,
dataset=data,
extensions=[
MomentumAdjustor(final_momentum=0.9, start=0,
saturate=25),
LinearDecayOverEpoch(start=1,
saturate=25,
decay_factor=.02)
])
def get_train_sgd(self, config_id):
row = self.db.executeSQL(
"""
SELECT learning_rate,batch_size,init_momentum,
train_iteration_mode,cost_array,term_array
FROM hps3.train_sgd
WHERE config_id = %s
""", (config_id, ), self.db.FETCH_ONE)
if not row or row is None:
raise HPSData("No stochasticGradientDescent for config_id="\
+str(config_id))
(learning_rate, batch_size, init_momentum, train_iteration_mode,
cost_array, term_array) = row
# cost
cost = self.get_costs(cost_array)
num_train_batch = (self.ntrain / self.batch_size)
print "num training batches:", num_train_batch
termination_criterion \
= self.get_terminations(config_id, term_array)
return SGD(learning_rate=learning_rate,
cost=cost,
batch_size=batch_size,
batches_per_iter=num_train_batch,
monitoring_dataset=self.monitoring_dataset,
termination_criterion=termination_criterion,
init_momentum=init_momentum,
train_iteration_mode=train_iteration_mode)
def test_sgd_sup():
# tests that we can run the sgd algorithm
# on a supervised cost.
# does not test for correctness at all, just
# that the algorithm runs without dying
dim = 3
m = 10
rng = np.random.RandomState([25, 9, 2012])
X = rng.randn(m, dim)
idx = rng.randint(0, dim, (m, ))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
dataset = DenseDesignMatrix(X=X, y=Y)
m = 15
X = rng.randn(m, dim)
idx = rng.randint(0, dim, (m,))
Y = np.zeros((m, dim))
for i in xrange(m):
Y[i, idx[i]] = 1
# Including a monitoring dataset lets us test that
# the monitor works with supervised data
monitoring_dataset = DenseDesignMatrix(X=X, y=Y)
model = SoftmaxModel(dim)
learning_rate = 1e-3
batch_size = 5
cost = SupervisedDummyCost()
# 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,
monitoring_batches=3,
monitoring_dataset=monitoring_dataset,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
train = Train(dataset,
model,
algorithm,
save_path=None,
save_freq=0,
extensions=None)
train.main_loop()
def train_model():
global ninput, noutput
simdata = SimulationData(
sim_path="../../javaDataCenter/generarDadesV1/CA_SDN_topo1/")
simdata.load_data()
simdata.preprocessor()
dataset = simdata.get_matrix()
structure = get_structure()
layers = []
for pair in structure:
layers.append(get_autoencoder(pair))
model = DeepComposedAutoencoder(layers)
training_alg = SGD(learning_rate=1e-3,
cost=MeanSquaredReconstructionError(),
batch_size=1296,
monitoring_dataset=dataset,
termination_criterion=EpochCounter(max_epochs=50))
extensions = [MonitorBasedLRAdjuster()]
experiment = Train(dataset=dataset,
model=model,
algorithm=training_alg,
save_path='training2.pkl',
save_freq=10,
allow_overwrite=True,
extensions=extensions)
experiment.main_loop()
def get_trainer(model, trainset, validset, epochs=20, batch_size=200):
monitoring_batches = None if validset is None else 20
train_algo = SGD(batch_size=batch_size,
init_momentum=0.5,
learning_rate=0.1,
monitoring_batches=monitoring_batches,
monitoring_dataset=validset,
cost=Dropout(input_include_probs={
'h0': 0.8,
'h1': 0.8,
'h2': 0.8,
'h3': 0.8,
'y': 0.5
},
input_scales={
'h0': 1. / 0.8,
'h1': 1. / 0.8,
'h2': 1. / 0.8,
'h3': 1. / 0.8,
'y': 1. / 0.5
},
default_input_include_prob=0.5,
default_input_scale=1. / 0.5),
termination_criterion=EpochCounter(epochs),
update_callbacks=ExponentialDecay(decay_factor=1.0001,
min_lr=0.001))
return Train(model=model, algorithm=train_algo, dataset=trainset, save_freq=0, save_path='epoch', \
extensions=[MomentumAdjustor(final_momentum=0.9, start=0, saturate=int(epochs*0.8)), ])
def get_train_sgd(self):
cost = MethodCost('cost_from_X')
#cost = self.get_costs()
num_train_batch = (self.ntrain/self.batch_size)
print "num training batches:", num_train_batch
termination_criterion = self.get_terminations()
monitoring_dataset = {}
for dataset_id in self.state.monitoring_dataset:
if dataset_id == 'test' and self.test_ddm is not None:
monitoring_dataset['test'] = self.test_ddm
elif dataset_id == 'valid' and self.valid_ddm is not None:
monitoring_dataset['valid'] = self.valid_ddm
else:
monitoring_dataset = None
return SGD( learning_rate=self.state.learning_rate,
batch_size=self.state.batch_size,
cost=cost,
batches_per_iter=num_train_batch,
monitoring_dataset=monitoring_dataset,
termination_criterion=termination_criterion,
init_momentum=self.state.init_momentum,
train_iteration_mode=self.state.train_iteration_mode)
def train_example(dataset=None):
model = GaussianBinaryRBM(nvis=1296,
nhid=61,
irange=0.5,
energy_function_class=grbm_type_1(),
learn_sigma=True,
init_sigma=.4,
init_bias_hid=2.,
mean_vis=False,
sigma_lr_scale=1e-3)
cost = SMD(corruptor=GaussianCorruptor(stdev=0.4))
algorithm = SGD(learning_rate=.1,
batch_size=5,
monitoring_batches=20,
monitoring_dataset=dataset,
cost=cost,
termination_criterion=MonitorBased(prop_decrease=0.01,
N=1))
train = Train(dataset=dataset,
model=model,
save_path="./experiment/training.pkl",
save_freq=10,
algorithm=algorithm,
extensions=[])
train.main_loop()
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 model1():
#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)
valid_set = MNIST(which_set='test', one_hot=True)
test_set = MNIST(which_set='test', one_hot=True)
#import pdb
#pdb.set_trace()
#print train_set.X.shape[1]
# =====<Create the MLP Model>=====
h2_layer = NoisyRELU(layer_name='h1',
sparse_init=15,
noise_factor=5,
dim=1000,
desired_active_rate=0.2,
bias_factor=20,
max_col_norm=1)
#h2_layer = RectifiedLinear(layer_name='h2', dim=100, sparse_init=15, max_col_norm=1)
#print h1_layer.get_params()
#h2 = RectifiedLinear(layer_name='h2', dim=500, sparse_init=15, max_col_norm=1)
y_layer = Softmax(layer_name='y', n_classes=10, irange=0., max_col_norm=1)
mlp = MLP(batch_size=200,
input_space=VectorSpace(dim=train_set.X.shape[1]),
layers=[h2_layer, y_layer])
# =====<Create the SGD algorithm>=====
sgd = SGD(init_momentum=0.1,
learning_rate=0.01,
monitoring_dataset={'valid': valid_set},
cost=MethodCost('cost_from_X'),
termination_criterion=MonitorBased(
channel_name='valid_y_misclass', prop_decrease=0.001, N=50))
#sgd.setup(model=mlp, dataset=train_set)
# =====<Extensions>=====
ext = [MomentumAdjustor(start=1, saturate=10, final_momentum=0.9)]
# =====<Create Training Object>=====
save_path = './mlp_model1.pkl'
train_obj = Train(dataset=train_set,
model=mlp,
algorithm=sgd,
extensions=ext,
save_path=save_path,
save_freq=0)
#train_obj.setup_extensions()
#import pdb
#pdb.set_trace()
train_obj.main_loop()
# =====<Run the training>=====
'''
def create_algorithm(self):
cost_crit = MonitorBased(channel_name=self.optimize_for,
prop_decrease=0.,
N=10)
epoch_cnt_crit = EpochCounter(max_epochs=self.max_epochs)
term = And(criteria=[cost_crit, epoch_cnt_crit])
self.algorithm = SGD(batch_size=100,
learning_rate=.01,
monitoring_dataset=self.alg_datasets,
termination_criterion=term)
def test_train_supervised():
"""
Train a supervised GSN.
"""
# initialize the GSN
gsn = GSN.new(
layer_sizes=[ds.X.shape[1], 1000, ds.y.shape[1]],
activation_funcs=["sigmoid", "tanh", rescaled_softmax],
pre_corruptors=[GaussianCorruptor(0.5)] * 3,
post_corruptors=[
SaltPepperCorruptor(.3), None,
SmoothOneHotCorruptor(.5)
],
layer_samplers=[BinomialSampler(), None,
MultinomialSampler()],
tied=False)
# average over costs rather than summing
_rcost = MeanBinaryCrossEntropy()
reconstruction_cost = lambda a, b: _rcost.cost(a, b) / ds.X.shape[1]
_ccost = MeanBinaryCrossEntropy()
classification_cost = lambda a, b: _ccost.cost(a, b) / ds.y.shape[1]
# combine costs into GSNCost object
c = GSNCost(
[
# reconstruction on layer 0 with weight 1.0
(0, 1.0, reconstruction_cost),
# classification on layer 2 with weight 2.0
(2, 2.0, classification_cost)
],
walkback=WALKBACK,
mode="supervised")
alg = SGD(
LEARNING_RATE,
init_momentum=MOMENTUM,
cost=c,
termination_criterion=EpochCounter(MAX_EPOCHS),
batches_per_iter=BATCHES_PER_EPOCH,
batch_size=BATCH_SIZE,
monitoring_dataset=ds,
monitoring_batches=10,
)
trainer = Train(ds,
gsn,
algorithm=alg,
save_path="gsn_sup_example.pkl",
save_freq=10,
extensions=[MonitorBasedLRAdjuster()])
trainer.main_loop()
print("done training")
def test_sgd_unspec_num_mon_batch():
# tests that if you don't specify a number of
# monitoring batches, SGD configures the monitor
# to run on all the data
m = 25
visited = [False] * m
rng = np.random.RandomState([25, 9, 2012])
X = np.zeros((m, 1))
X[:, 0] = np.arange(m)
dataset = DenseDesignMatrix(X=X)
model = SoftmaxModel(1)
learning_rate = 1e-3
batch_size = 5
cost = DummyCost()
algorithm = SGD(learning_rate,
cost,
batch_size=5,
monitoring_batches=None,
monitoring_dataset=dataset,
termination_criterion=None,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
algorithm.setup(dataset=dataset, model=model)
monitor = Monitor.get_monitor(model)
X = T.matrix()
def tracker(*data):
X, = data
assert X.shape[1] == 1
for i in xrange(X.shape[0]):
visited[int(X[i, 0])] = True
monitor.add_channel(name='tracker',
ipt=X,
val=0.,
prereqs=[tracker],
data_specs=(model.get_input_space(),
model.get_input_source()))
monitor()
if False in visited:
print visited
assert False
def testing_multiple_datasets_with_specified_dataset_in_monitor_based_lr():
# tests that the class MonitorBasedLRAdjuster in sgd.py can properly use
# the spcified dataset_name in the constructor when multiple datasets
# exist.
dim = 3
m = 10
rng = np.random.RandomState([06, 02, 2014])
X = rng.randn(m, dim)
Y = rng.randn(m, dim)
learning_rate = 1e-2
batch_size = 5
# We need to include this so the test actually stops running at some point
epoch_num = 1
# including a monitoring datasets lets us test that
# the monitor works with supervised data
monitoring_train = DenseDesignMatrix(X=X)
monitoring_test = DenseDesignMatrix(X=Y)
cost = DummyCost()
model = SoftmaxModel(dim)
dataset = DenseDesignMatrix(X=X)
termination_criterion = EpochCounter(epoch_num)
monitoring_dataset = {'train': monitoring_train, 'test': monitoring_test}
algorithm = SGD(learning_rate,
cost,
batch_size=batch_size,
monitoring_batches=2,
monitoring_dataset=monitoring_dataset,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
dataset_name = monitoring_dataset.keys()[0]
monitor_lr = MonitorBasedLRAdjuster(dataset_name=dataset_name)
train = Train(dataset,
model,
algorithm,
save_path=None,
save_freq=0,
extensions=[monitor_lr])
train.main_loop()
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 set_training_criteria(self, learning_rate=0.05, cost=MeanSquaredReconstructionError(), batch_size=10, max_epochs=10): self.training_alg = SGD(learning_rate = learning_rate, cost = cost, batch_size = batch_size, monitoring_dataset = self.datasets, termination_criterion = EpochCounter(max_epochs))
def set_training_criteria(self,
learning_rate=0.05,
cost=Default(),
batch_size=10,
max_epochs=10):
self.training_alg = SGD(learning_rate=learning_rate,
cost=cost,
batch_size=batch_size,
monitoring_dataset=self.datasets,
termination_criterion=EpochCounter(max_epochs))
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 __init__(self,
runner,
model_params,
resume=False,
resume_data=None,
s3_data=None,
**kwargs):
self.model_params = model_params
self.out_nonlin = runner.model['out_nonlin']
if self.out_nonlin == 'LINEARGAUSSIAN':
outputs_num = None
cost = None
else:
outputs_num = runner.dp.uniq_outputs_num
cost = self.get_cost_fn()
dataset = self.construct_datasets(runner.dp.train_set_x,
runner.dp.train_set_y, outputs_num)
valid_dataset = self.construct_datasets(runner.dp.test_set_x,
runner.dp.test_set_y,
outputs_num)
if resume:
model = self.resume_model(model_params, resume_data)
lr_init = model_params['learning_rate']['init'] / (
model_params['learning_rate']['decay_factor']**
model.monitor.get_batches_seen())
else:
model = self.new_model(model_params, dataset=dataset)
lr_init = model_params['learning_rate']['init']
batches_per_iter = get_batches_per_iter(model_params, dataset)
termination_criterion = MaxEpochNumber(model_params['maxnum_iter'])
update_callbacks, extensions = construct_update(
model_params, resume, resume_data)
algorithm = SGD(learning_rate=lr_init,
init_momentum=model_params['momentum']['init'],
monitoring_dataset={
'valid': valid_dataset,
'train': dataset
},
cost=cost,
termination_criterion=termination_criterion,
update_callbacks=update_callbacks,
batches_per_iter=batches_per_iter)
self.train_obj = Train(dataset=dataset,
model=model,
algorithm=algorithm,
extensions=extensions)
ext = MLPStatReporter(model,
runner,
resume=resume,
resume_data=resume_data,
save_freq=model_params['save_freq'])
self.train_obj.extensions.append(ext)
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_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 model3():
#pdb.set_trace()
# train set X has dim (60,000, 784), y has dim (60,000, 10)
train_set = SVHN_On_Memory(which_set='train')
# test set X has dim (10,000, 784), y has dim (10,000, 10)
test_set = SVHN_On_Memory(which_set='test')
# =====<Create the MLP Model>=====
h1_layer = NoisyRELU(layer_name='h1',
dim=2000,
threshold=5,
sparse_init=15,
max_col_norm=1)
#print h1_layer.get_params()
#h2_layer = NoisyRELU(layer_name='h2', dim=100, threshold=15, 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=64,
input_space=VectorSpace(dim=train_set.X.shape[1]),
layers=[h1_layer, y_layer])
# =====<Create the SGD algorithm>=====
sgd = SGD(batch_size=64,
init_momentum=0.1,
learning_rate=0.01,
monitoring_dataset={
'valid': train_set,
'test': test_set
},
cost=MethodCost('cost_from_X'),
termination_criterion=MonitorBased(
channel_name='valid_y_misclass', prop_decrease=0.001, N=50))
#sgd.setup(model=mlp, dataset=train_set)
# =====<Extensions>=====
ext = [MomentumAdjustor(start=1, saturate=10, final_momentum=0.9)]
# =====<Create Training Object>=====
save_path = './mlp_model.pkl'
train_obj = Train(dataset=train_set,
model=mlp,
algorithm=sgd,
extensions=ext,
save_path=save_path,
save_freq=10)
#train_obj.setup_extensions()
train_obj.main_loop()
