本文整理汇总了Python中pylearn2.monitor.Monitor.get_monitor方法的典型用法代码示例。如果您正苦于以下问题:Python Monitor.get_monitor方法的具体用法?Python Monitor.get_monitor怎么用?Python Monitor.get_monitor使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pylearn2.monitor.Monitor
的用法示例。
在下文中一共展示了Monitor.get_monitor方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_serialization_guard
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def test_serialization_guard():
# tests that Train refuses to serialize the dataset
dim = 2
m = 11
rng = np.random.RandomState([28,9,2012])
X = rng.randn(m, dim)
dataset = DenseDesignMatrix(X=X)
model = DummyModel(dim)
# make the dataset part of the model, so it will get
# serialized
model.dataset = dataset
Monitor.get_monitor(model)
algorithm = DummyAlgorithm()
train = Train(dataset, model, algorithm, save_path='_tmp_unit_test.pkl',
save_freq=1, extensions=None)
try:
train.main_loop()
except RuntimeError:
return
assert False # train did not complain, this is a bug
示例2: test_reject_empty
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def test_reject_empty():
# Test that Monitor raises an error if asked to iterate over 0 batches
BATCH_SIZE = 2
num_examples = BATCH_SIZE
NUM_FEATURES = 3
model = DummyModel(NUM_FEATURES)
monitor = Monitor.get_monitor(model)
monitoring_dataset = DummyDataset(num_examples = num_examples,
num_features = NUM_FEATURES)
monitor.add_dataset(monitoring_dataset, 'sequential', batch_size=BATCH_SIZE,
num_batches = 0)
name = 'z'
monitor.add_channel(name = name,
ipt = model.input_space.make_theano_batch(),
val = 0.,
data_specs=(model.get_input_space(), model.get_input_source()))
try:
monitor()
except ValueError:
return
assert False
示例3: test_prereqs
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def test_prereqs():
# Test that prereqs get run before the monitoring channels are computed
BATCH_SIZE = 2
num_examples = BATCH_SIZE
NUM_FEATURES = 3
model = DummyModel(NUM_FEATURES)
monitor = Monitor.get_monitor(model)
monitoring_dataset = DummyDataset(num_examples = num_examples,
num_features = NUM_FEATURES)
monitor.add_dataset(monitoring_dataset, 'sequential', batch_size=BATCH_SIZE)
prereq_counter = sharedX(0.)
def prereq(*data):
prereq_counter.set_value(prereq_counter.get_value() + 1.)
name = 'num_prereq_calls'
monitor.add_channel(name = name,
ipt = model.input_space.make_theano_batch(),
val = prereq_counter,
prereqs = [ prereq ],
data_specs=(model.get_input_space(), model.get_input_source()))
channel = monitor.channels[name]
assert len(channel.val_record) == 0
monitor()
assert channel.val_record == [1]
monitor()
assert channel.val_record == [1,2]
示例4: setup
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def setup(self, model, dataset):
"""
Allows the training algorithm to do some preliminary configuration
*before* we actually start training the model. The dataset is provided
in case other derived training algorithms need to modify model based on
the dataset.
Parameters
----------
model: a Python object representing the model to train loosely
implementing the interface of models.model.Model.
dataset: a pylearn2.datasets.dataset.Dataset object used to draw
training data
"""
self.model = model
self.monitor = Monitor.get_monitor(model)
if self.monitoring_dataset is not None:
# Get the data specifications needed by the model
space, source = model.get_monitoring_data_specs()
# Create Theano variables for each of the individual components
# of that data. Usually, it will be X for inputs and Y for targets.
# First, we need to find these components, and put them in a tuple
mapping = DataSpecsMapping((space, source))
space_tuple = mapping.flatten(space, return_tuple=True)
source_tuple = mapping.flatten(source, return_tuple=True)
# Then, build a flat tuple of these Theano variables
ipt = tuple(sp.make_theano_batch(name='monitor_%s' % src)
for (sp, src) in safe_zip(space_tuple, source_tuple))
# Finally, organize them back into a structure expected by the
# monitoring channels of the model
nested_ipt = mapping.nest(ipt)
self.monitor.add_dataset(dataset=self.monitoring_dataset,
mode="sequential",
batch_size=self.batch_size,
num_batches=self.monitoring_batches)
channels = model.get_monitoring_channels(nested_ipt)
if not isinstance(channels, dict):
raise TypeError("model.get_monitoring_channels must return a "
"dictionary, but it returned " + str(channels))
for name in channels:
J = channels[name]
if isinstance(J, tuple):
assert len(J) == 2
J, prereqs = J
else:
prereqs = None
self.monitor.add_channel(name=name,
ipt=nested_ipt,
val=J,
prereqs=prereqs,
data_specs=(space, source))
self.first = True
self.bSetup = True
示例5: prep_valtest_monitor
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def prep_valtest_monitor(self, model, batch_size):
minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_topo(batch_size),
name='minibatch'
)
target = T.matrix('target')
Accuracy = self.get_classification_accuracy(model, minibatch, target)
monitor = Monitor.get_monitor(model)
monitor.add_dataset(self.valid_ddm, 'sequential', batch_size)
monitor.add_channel("Validation Classification Accuracy",
(minibatch, target),
Accuracy,
self.valid_ddm)
monitor.add_channel("Validation Missclassification",
(minibatch, target),
1.0-Accuracy,
self.valid_ddm)
if self.test_ddm is not None:
monitor.add_dataset(self.test_ddm, 'sequential', batch_size)
monitor.add_channel("Test Classification Accuracy",
(minibatch, target),
Accuracy,
self.test_ddm)
示例6: setup_monitor
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def setup_monitor(self):
if self.topo_view:
print "topo view"
self.minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_topo(self.batch_size),
name='minibatch'
)
else:
print "design view"
self.minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_design(self.batch_size),
name='minibatch'
)
self.target = T.tensor3('target')
self.monitor = Monitor.get_monitor(self.model)
self.log_channel_names = []
self.log_channel_names.extend(self.base_channel_names)
self.monitor.add_dataset(self.valid_ddm, 'sequential',
self.batch_size)
if self.test_ddm is not None:
self.monitor.add_dataset(self.test_ddm, 'sequential',
self.batch_size)
示例7: setup_monitor
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def setup_monitor(self):
if self.topo_view:
print "topo view"
self.minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_topo(self.batch_size),
name='minibatch'
)
else:
print "design view"
batch = self.valid_ddm.get_batch_design(self.batch_size)
if isinstance(batch, spp.csr_matrix):
print "sparse2"
self.minibatch = self.model.get_input_space().make_batch_theano()
print type(self.minibatch)
else:
self.minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_design(self.batch_size),
name='minibatch'
)
self.target = T.matrix('target')
self.monitor = Monitor.get_monitor(self.model)
self.log_channel_names = []
self.log_channel_names.extend(self.base_channel_names)
self.monitor.add_dataset(self.valid_ddm, 'sequential',
self.batch_size)
if self.test_ddm is not None:
self.monitor.add_dataset(self.test_ddm, 'sequential',
self.batch_size)
示例8: main_loop
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def main_loop(self):
"""
Repeatedly runs an epoch of the training algorithm, runs any
epoch-level callbacks, and saves the model.
"""
if self.algorithm is None:
self.model.monitor = Monitor.get_monitor(self.model)
self.run_callbacks_and_monitoring()
while self.model.train(dataset=self.dataset):
self.run_callbacks_and_monitoring()
if self.save_freq > 0 and self.epochs % self.save_freq == 0:
self.save()
self.epochs += 1
self.run_callbacks_and_monitoring()
if self.save_freq > 0:
self.save()
else:
self.algorithm.setup(model=self.model, dataset=self.dataset)
self.run_callbacks_and_monitoring()
epoch_start = datetime.datetime.now()
while self.algorithm.train(dataset=self.dataset):
epoch_end = datetime.datetime.now()
print 'Time this epoch:', str(epoch_end - epoch_start)
epoch_start = datetime.datetime.now()
self.run_callbacks_and_monitoring()
if self.save_freq > 0 and self.epochs % self.save_freq == 0:
self.save()
self.epochs += 1
self.run_callbacks_and_monitoring()
if self.save_freq > 0:
self.save()
示例9: test_ambig_data
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def test_ambig_data():
# test that the right error is raised if you
# add a channel to a monitor that has multiple datasets
# and don't specify the dataset
BATCH_SIZE = 2
num_examples = BATCH_SIZE
NUM_FEATURES = 3
model = DummyModel(NUM_FEATURES)
monitor = Monitor.get_monitor(model)
first = DummyDataset(num_examples = num_examples,
num_features = NUM_FEATURES)
second = DummyDataset(num_examples = num_examples,
num_features = NUM_FEATURES)
monitor.add_dataset(first, 'sequential', batch_size=BATCH_SIZE)
monitor.add_dataset(second, 'sequential', batch_size=BATCH_SIZE)
name = 'num_prereq_calls'
try:
monitor.add_channel(name = name,
ipt = model.input_space.make_theano_batch(),
val = 0.,
data_specs=(model.get_input_space(), model.get_input_source()))
except ValueError as e:
assert exc_message(e) == _err_ambig_data
return
assert False
示例10: test_save_load_save
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def test_save_load_save():
"""
Test that a monitor can be saved, then loaded, and then the loaded
copy can be saved again.
This only tests that the serialization and deserialization processes
don't raise an exception. It doesn't test for correctness at all.
"""
model = DummyModel(1)
monitor = Monitor.get_monitor(model)
num_examples = 2
num_features = 3
num_batches = 1
batch_size = 2
dataset = DummyDataset(num_examples, num_features)
monitor.add_dataset(dataset=dataset,
num_batches=num_batches, batch_size=batch_size)
vis_batch = T.matrix()
mean = vis_batch.mean()
data_specs = (monitor.model.get_input_space(),
monitor.model.get_input_source())
monitor.add_channel(name='mean', ipt=vis_batch, val=mean, dataset=dataset,
data_specs=data_specs)
saved = to_string(monitor)
monitor = from_string(saved)
saved_again = to_string(monitor)
示例11: test_prereqs_batch
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def test_prereqs_batch():
# Test that prereqs get run before each monitoring batch
BATCH_SIZE = 2
num_examples = 2 * BATCH_SIZE
NUM_FEATURES = 3
model = DummyModel(NUM_FEATURES)
monitor = Monitor.get_monitor(model)
monitoring_dataset = DummyDataset(num_examples=num_examples, num_features=NUM_FEATURES)
monitor.add_dataset(monitoring_dataset, "sequential", batch_size=BATCH_SIZE)
sign = sharedX(1.0)
def prereq(X, y):
sign.set_value(-sign.get_value())
name = "batches_should_cancel_to_0"
monitor.add_channel(name=name, ipt=model.input_space.make_theano_batch(), val=sign, prereqs=[prereq])
channel = monitor.channels[name]
assert len(channel.val_record) == 0
monitor()
assert channel.val_record == [0]
monitor()
assert channel.val_record == [0, 0]
示例12: prep_valtest_monitor
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def prep_valtest_monitor(self, model, batch_size):
if self.topo_view:
print "topo view"
minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_topo(batch_size),
name='minibatch'
)
else:
print "design view"
minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_design(batch_size),
name='minibatch'
)
target = T.matrix('target')
valMSE = MissingTargetCost()(model, minibatch, target)
monitor = Monitor.get_monitor(model)
monitor.add_dataset(self.valid_ddm, 'sequential', batch_size)
monitor.add_channel("Validation MSE",
(minibatch, target),
valMSE,
self.valid_ddm)
if self.test_ddm is not None:
monitor.add_dataset(self.test_ddm, 'sequential', batch_size)
monitor.add_channel("Test MSE",
(minibatch, target),
valMSE,
self.test_ddm)
示例13: setup
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def setup(self, model, dataset):
"""
Initialize the training algorithm. Should be called
once before calls to train.
Parameters
----------
model : object
Model to be trained. Object implementing the pylearn2 Model
interface.
dataset : object
Dataset on which to train. Object implementing the
pylearn2 Dataset interface.
"""
self.model = model
self.monitor = Monitor.get_monitor(model)
self.monitor.set_dataset(dataset=self.monitoring_dataset,
batches=self.monitoring_batches,
batch_size=self.batch_size)
X = T.matrix(name='sgd_X')
J = self.cost(model, X)
if J.name is None:
J.name = 'sgd_cost(' + X.name + ')'
self.monitor.add_channel(name=J.name, ipt=X, val=J)
params = model.get_params()
for i, param in enumerate(params):
if param.name is None:
param.name = 'sgd_params[%d]' % i
grads = dict(zip(params, T.grad(J, params)))
for param in grads:
if grads[param].name is None:
grads[param].name = ('grad(%(costname)s, %(paramname)s)' %
{'costname': J.name,
'paramname': param.name})
learning_rate = T.scalar('sgd_learning_rate')
updates = dict(zip(params, [param - learning_rate * grads[param]
for param in params]))
for param in updates:
if updates[param].name is None:
updates[param].name = 'sgd_update(' + param.name + ')'
model.censor_updates(updates)
for param in updates:
if updates[param] is None:
updates[param].name = 'censor(sgd_update(' + param.name + '))'
self.sgd_update = function([X, learning_rate], updates=updates,
name='sgd_update')
self.params = params
self.bSetup = True
示例14: test_deserialize
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
def test_deserialize():
# Test that a monitor can be deserialized
model = DummyModel(1)
monitor = Monitor.get_monitor(model)
x = to_string(monitor)
monitor = from_string(x)
y = to_string(monitor)
示例15: test_sgd_unspec_num_mon_batch
# 需要导入模块: from pylearn2.monitor import Monitor [as 别名]
# 或者: from pylearn2.monitor.Monitor import get_monitor [as 别名]
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=batch_size,
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