def test_variational_cd():
# Verifies that VariationalCD works well with make_layer_to_symbolic_state
visible_layer = BinaryVector(nvis=100)
hidden_layer = BinaryVectorMaxPool(detector_layer_dim=500,
pool_size=1,
layer_name='h',
irange=0.05,
init_bias=-2.0)
model = DBM(visible_layer=visible_layer,
hidden_layers=[hidden_layer],
batch_size=100,
niter=1)
cost = VariationalCD(num_chains=100, num_gibbs_steps=2)
data_specs = cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s' % (source)
arg = space.make_theano_batch(name=name)
theano_args.append(arg)
theano_args = tuple(theano_args)
nested_args = mapping.nest(theano_args)
grads, updates = cost.get_gradients(model, nested_args)
def get_gradients(model):
cost = model.get_default_cost()
data_specs = cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (SGD.__class__.__name__, source)
arg = space.make_theano_batch(name=name, batch_size=model.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
nested_args = mapping.nest(theano_args)
fixed_var_descr = cost.get_fixed_var_descr(model, nested_args)
grads, updates = cost.get_gradients(model, nested_args,
**fixed_var_descr.fixed_vars)
params = list(model.get_params())
for param in params:
some = grads[param]
print("ok")
return grads
def test_variational_cd():
# Verifies that VariationalCD works well with make_layer_to_symbolic_state
visible_layer = BinaryVector(nvis=100)
hidden_layer = BinaryVectorMaxPool(detector_layer_dim=500,
pool_size=1,
layer_name='h',
irange=0.05,
init_bias=-2.0)
model = DBM(visible_layer=visible_layer,
hidden_layers=[hidden_layer],
batch_size=100,
niter=1)
cost = VariationalCD(num_chains=100, num_gibbs_steps=2)
data_specs = cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s' % (source)
arg = space.make_theano_batch(name=name)
theano_args.append(arg)
theano_args = tuple(theano_args)
nested_args = mapping.nest(theano_args)
grads, updates = cost.get_gradients(model, nested_args)
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
def _build_data_specs(self):
"""
Computes a nested data_specs for input and all channels
Also computes the mapping to flatten it. This function is called from
redo_theano.
"""
# Ask the model what it needs
m_space, m_source = self.model.get_monitoring_data_specs()
input_spaces = [m_space]
input_sources = [m_source]
for channel in self.channels.values():
space = channel.data_specs[0]
assert isinstance(space, Space)
input_spaces.append(space)
input_sources.append(channel.data_specs[1])
nested_space = CompositeSpace(input_spaces)
nested_source = tuple(input_sources)
self._nested_data_specs = (nested_space, nested_source)
self._data_specs_mapping = DataSpecsMapping(self._nested_data_specs)
flat_space = self._data_specs_mapping.flatten(nested_space,
return_tuple=True)
flat_source = self._data_specs_mapping.flatten(nested_source,
return_tuple=True)
self._flat_data_specs = (CompositeSpace(flat_space), flat_source)
def train(self, dataset):
if not hasattr(self, 'sgd_update'):
raise Exception("train called without first calling setup")
# Make sure none of the parameters have bad values
for param in self.params:
value = param.get_value(borrow=True)
if np.any(np.isnan(value)) or np.any(np.isinf(value)):
raise Exception("NaN in " + param.name)
self.first = False
rng = self.rng
if not is_stochastic(self.train_iteration_mode):
rng = None
data_specs = self.cost.get_data_specs(self.model)
# The iterator should be built from flat data specs, so it returns
# flat, non-redundent tuples of data.
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
if len(space_tuple) == 0:
# No data will be returned by the iterator, and it is impossible
# to know the size of the actual batch.
# It is not decided yet what the right thing to do should be.
raise NotImplementedError(
"Unable to train with SGD, because "
"the cost does not actually use data from the data set. "
"data_specs: %s" % str(data_specs))
flat_data_specs = (CompositeSpace(space_tuple), source_tuple)
iterator = dataset.iterator(mode=self.train_iteration_mode,
batch_size=self.batch_size,
data_specs=flat_data_specs,
return_tuple=True,
rng=rng,
num_batches=self.batches_per_iter)
on_load_batch = self.on_load_batch
for batch in iterator:
for callback in on_load_batch:
callback(mapping.nest(batch))
self.sgd_update(*batch)
# iterator might return a smaller batch if dataset size
# isn't divisible by batch_size
# Note: if data_specs[0] is a NullSpace, there is no way to know
# how many examples would actually have been in the batch,
# since it was empty, so actual_batch_size would be reported as 0.
actual_batch_size = flat_data_specs[0].np_batch_size(batch)
self.monitor.report_batch(actual_batch_size)
for callback in self.update_callbacks:
callback(self)
# Make sure none of the parameters have bad values
for param in self.params:
value = param.get_value(borrow=True)
if np.any(np.isnan(value)) or np.any(np.isinf(value)):
raise Exception("NaN in " + param.name)
def load_model(model_paths, costs, batch_size=100):
if type(costs) is not list:
costs = len(model_paths) * [costs]
model = {}
model['layers'] = []
model['costs'] = []
model['comparative_costs'] = []
model['weights'] = []
model['encoders'] = []
model['decoders'] = []
for i, path in enumerate(model_paths):
if os.path.isfile(path):
model['layers'].append(serial.load(path))
I = model['layers'][i].get_input_space().make_theano_batch(
batch_size=batch_size)
E = model['layers'][i].encode(I)
model['encoders'].append(theano.function([I], E))
H = model['layers'][i].get_output_space().make_theano_batch(
batch_size=batch_size)
D = model['layers'][i].decode(H)
model['decoders'].append(theano.function([H], D))
model['weights'].append(model['layers'][i].get_weights())
data_specs = costs[i].get_data_specs(model['layers'][i])
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
arg = space.make_theano_batch(batch_size=batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = costs[i].get_fixed_var_descr(
model['layers'][i], nested_args)
model['costs'].append(
theano.function([nested_args],
costs[i].expr(model['layers'][i], nested_args,
**fixed_var_descr.fixed_vars)))
I2 = model['layers'][i].get_input_space().make_theano_batch(
batch_size=batch_size)
model['comparative_costs'].append(
theano.function([I, I2], costs[i].costs[0].cost(I, I2)))
else:
sys.exit("Whoa. " + path + " isn't a thing I know about!")
return model
def train(self, dataset):
if not hasattr(self, 'sgd_update'):
raise Exception("train called without first calling setup")
# Make sure none of the parameters have bad values
for param in self.params:
value = param.get_value(borrow=True)
if np.any(np.isnan(value)) or np.any(np.isinf(value)):
raise Exception("NaN in " + param.name)
self.first = False
rng = self.rng
if not is_stochastic(self.train_iteration_mode):
rng = None
data_specs = self.cost.get_data_specs(self.model)
# The iterator should be built from flat data specs, so it returns
# flat, non-redundent tuples of data.
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
if len(space_tuple) == 0:
# No data will be returned by the iterator, and it is impossible
# to know the size of the actual batch.
# It is not decided yet what the right thing to do should be.
raise NotImplementedError("Unable to train with SGD, because "
"the cost does not actually use data from the data set. "
"data_specs: %s" % str(data_specs))
flat_data_specs = (CompositeSpace(space_tuple), source_tuple)
iterator = dataset.iterator(mode=self.train_iteration_mode,
batch_size=self.batch_size,
data_specs=flat_data_specs, return_tuple=True,
rng = rng, num_batches = self.batches_per_iter)
on_load_batch = self.on_load_batch
for batch in iterator:
for callback in on_load_batch:
callback(mapping.nest(batch))
self.sgd_update(*batch)
# iterator might return a smaller batch if dataset size
# isn't divisible by batch_size
# Note: if data_specs[0] is a NullSpace, there is no way to know
# how many examples would actually have been in the batch,
# since it was empty, so actual_batch_size would be reported as 0.
actual_batch_size = flat_data_specs[0].np_batch_size(batch)
self.monitor.report_batch(actual_batch_size)
for callback in self.update_callbacks:
callback(self)
# Make sure none of the parameters have bad values
for param in self.params:
value = param.get_value(borrow=True)
if np.any(np.isnan(value)) or np.any(np.isinf(value)):
raise Exception("NaN in " + param.name)
def train(self, dataset):
"""
.. todo::
WRITEME
"""
assert self.bSetup
model = self.model
rng = self.rng
train_iteration_mode = "shuffled_sequential"
if not is_stochastic(train_iteration_mode):
rng = None
data_specs = self.cost.get_data_specs(self.model)
# The iterator should be built from flat data specs, so it returns
# flat, non-redundent tuples of data.
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
if len(space_tuple) == 0:
# No data will be returned by the iterator, and it is impossible
# to know the size of the actual batch.
# It is not decided yet what the right thing to do should be.
raise NotImplementedError(
"Unable to train with BGD, because "
"the cost does not actually use data from the data set. "
"data_specs: %s" % str(data_specs)
)
flat_data_specs = (CompositeSpace(space_tuple), source_tuple)
iterator = dataset.iterator(
mode=train_iteration_mode,
batch_size=self.batch_size,
num_batches=self.batches_per_iter,
data_specs=flat_data_specs,
return_tuple=True,
rng=rng,
)
mode = self.theano_function_mode
for data in iterator:
if "targets" in source_tuple and mode is not None and hasattr(mode, "record"):
Y = data[source_tuple.index("targets")]
stry = str(Y).replace("\n", " ")
mode.record.handle_line("data Y " + stry + "\n")
for on_load_batch in self.on_load_batch:
on_load_batch(mapping.nest(data))
self.before_step(model)
self.optimizer.minimize(*data)
self.after_step(model)
actual_batch_size = flat_data_specs[0].np_batch_size(data)
model.monitor.report_batch(actual_batch_size)
def setup(self):
self.X = T.matrix('X')
self.Y = T.matrix('Y')
# Taken from pylearn2/training_algorithms/sgd.py
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
print 'BATCH SIZE=', self.batch_size
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
print self.cost
fixed_var_descr = self.cost.get_fixed_var_descr(
self.model, nested_args)
print self.cost
self.on_load_batch = fixed_var_descr.on_load_batch
params = list(self.model.get_params())
self.X = nested_args[0]
self.Y = nested_args[1]
init_grads, updates = self.cost.get_gradients(self.model, nested_args)
params = self.model.get_params()
# We need to replace parameters with purely symbolic variables in case some are shared
# Create gradient and cost functions
self.params = params
symbolic_params = [self._convert_variable(param) for param in params]
givens = dict(zip(params, symbolic_params))
costfn = self.model.cost_from_X((self.X, self.Y))
gradfns = [init_grads[param] for param in params]
#self.symbolic_params = symbolic_params
#self._loss = theano.function(symbolic_para[self.X, self.Y], self.model.cost_from_X((self.X, self.Y)))#, givens=givens)
#1/0
print 'Compiling function...'
self.theano_f_df = theano.function(inputs=symbolic_params +
[self.X, self.Y],
outputs=[costfn] + gradfns,
givens=givens)
print 'done'
def __init__(self, data_callbacks, data_specs):
"""
data_callback: optional, callbacks to run on data.
It is either a Python callable, or a tuple (possibly nested),
in the same format as data_specs.
data_specs: (space, source) pair specifying the format
and label associated to the data.
"""
self.data_callbacks = data_callbacks
self.data_specs = data_specs
self._mapping = DataSpecsMapping(data_specs)
def train(self, dataset):
"""
.. todo::
WRITEME
"""
assert self.bSetup
model = self.model
rng = self.rng
train_iteration_mode = 'shuffled_sequential'
if not is_stochastic(train_iteration_mode):
rng = None
data_specs = self.cost.get_data_specs(self.model)
# The iterator should be built from flat data specs, so it returns
# flat, non-redundent tuples of data.
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
if len(space_tuple) == 0:
# No data will be returned by the iterator, and it is impossible
# to know the size of the actual batch.
# It is not decided yet what the right thing to do should be.
raise NotImplementedError(
"Unable to train with BGD, because "
"the cost does not actually use data from the data set. "
"data_specs: %s" % str(data_specs))
flat_data_specs = (CompositeSpace(space_tuple), source_tuple)
iterator = dataset.iterator(mode=train_iteration_mode,
batch_size=self.batch_size,
num_batches=self.batches_per_iter,
data_specs=flat_data_specs,
return_tuple=True,
rng=rng)
mode = self.theano_function_mode
for data in iterator:
if ('targets' in source_tuple and mode is not None
and hasattr(mode, 'record')):
Y = data[source_tuple.index('targets')]
stry = str(Y).replace('\n', ' ')
mode.record.handle_line('data Y ' + stry + '\n')
for on_load_batch in self.on_load_batch:
on_load_batch(mapping.nest(data))
self.before_step(model)
self.optimizer.minimize(*data)
self.after_step(model)
actual_batch_size = flat_data_specs[0].np_batch_size(data)
model.monitor.report_batch(actual_batch_size)
def setup(self):
self.X = T.matrix('X')
self.Y = T.matrix('Y')
# Taken from pylearn2/training_algorithms/sgd.py
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name, batch_size = self.batch_size)
theano_args.append(arg)
print 'BATCH SIZE=',self.batch_size
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
print self.cost
fixed_var_descr = self.cost.get_fixed_var_descr(self.model, nested_args)
print self.cost
self.on_load_batch = fixed_var_descr.on_load_batch
params = list(self.model.get_params())
self.X = nested_args[0]
self.Y = nested_args[1]
init_grads, updates = self.cost.get_gradients(self.model, nested_args)
params = self.model.get_params()
# We need to replace parameters with purely symbolic variables in case some are shared
# Create gradient and cost functions
self.params = params
symbolic_params = [self._convert_variable(param) for param in params]
givens = dict(zip(params, symbolic_params))
costfn = self.model.cost_from_X((self.X, self.Y))
gradfns = [init_grads[param] for param in params]
#self.symbolic_params = symbolic_params
#self._loss = theano.function(symbolic_para[self.X, self.Y], self.model.cost_from_X((self.X, self.Y)))#, givens=givens)
#1/0
print 'Compiling function...'
self.theano_f_df = theano.function(inputs=symbolic_params + [self.X, self.Y], outputs=[costfn] + gradfns, givens=givens)
print 'done'
def test_nest_specs():
x1 = TT.matrix('x1')
x2 = TT.matrix('x2')
x3 = TT.matrix('x3')
x4 = TT.matrix('x4')
for nested_space, nested_source, nested_data in [
(VectorSpace(dim=10), 'target', x2),
(CompositeSpace([VectorSpace(dim=3), VectorSpace(dim=9)]),
('features', 'features'),
(x1, x4)),
(CompositeSpace([VectorSpace(dim=3),
CompositeSpace([VectorSpace(dim=10),
VectorSpace(dim=7)])]),
('features', ('target', 'features')),
(x1, (x2, x3))),
]:
mapping = DataSpecsMapping((nested_space, nested_source))
flat_space = mapping.flatten(nested_space)
flat_source = mapping.flatten(nested_source)
flat_data = mapping.flatten(nested_data)
renested_space = mapping.nest(flat_space)
renested_source = mapping.nest(flat_source)
renested_data = mapping.nest(flat_data)
assert_equal(renested_space, nested_space)
assert_equal(renested_source, nested_source)
assert_equal(renested_data, nested_data)
class CallbackCost(Cost):
"""
A Cost that runs callbacks on the data.
Returns the sum of the data multiplied by the
sum of all model parameters as the cost.
The callback is run via the CallbackOp
so the cost must be used to compute one
of the outputs of your theano graph if you
want the callback to get called.
The is cost is designed so that the SGD algorithm
will result in in the CallbackOp getting
evaluated.
"""
def __init__(self, data_callbacks, data_specs):
"""
data_callback: optional, callbacks to run on data.
It is either a Python callable, or a tuple (possibly nested),
in the same format as data_specs.
data_specs: (space, source) pair specifying the format
and label associated to the data.
"""
self.data_callbacks = data_callbacks
self.data_specs = data_specs
self._mapping = DataSpecsMapping(data_specs)
def get_data_specs(self, model):
return self.data_specs
def expr(self, model, data):
self.get_data_specs(model)[0].validate(data)
callbacks = self.data_callbacks
cb_tuple = self._mapping.flatten(callbacks, return_tuple=True)
data_tuple = self._mapping.flatten(data, return_tuple=True)
costs = []
for (callback, data_var) in safe_zip(cb_tuple, data_tuple):
orig_var = data_var
data_var = CallbackOp(callback)(data_var)
assert len(data_var.owner.inputs) == 1
assert orig_var is data_var.owner.inputs[0]
costs.append(data_var.sum())
# sum() will call theano.add on the symbolic variables
cost = sum(costs)
model_terms = sum([param.sum() for param in model.get_params()])
cost = cost * model_terms
return cost
class CallbackCost(Cost):
"""
A Cost that runs callbacks on the data.
Returns the sum of the data multiplied by the
sum of all model parameters as the cost.
The callback is run via the CallbackOp
so the cost must be used to compute one
of the outputs of your theano graph if you
want the callback to get called.
The is cost is designed so that the SGD algorithm
will result in in the CallbackOp getting
evaluated.
"""
def __init__(self, data_callbacks, data_specs):
"""
data_callback: optional, callbacks to run on data.
It is either a Python callable, or a tuple (possibly nested),
in the same format as data_specs.
data_specs: (space, source) pair specifying the format
and label associated to the data.
"""
self.data_callbacks = data_callbacks
self.data_specs = data_specs
self._mapping = DataSpecsMapping(data_specs)
def get_data_specs(self, model):
return self.data_specs
def expr(self, model, data):
self.get_data_specs(model)[0].validate(data)
callbacks = self.data_callbacks
cb_tuple = self._mapping.flatten(callbacks, return_tuple=True)
data_tuple = self._mapping.flatten(data, return_tuple=True)
costs = []
for (callback, data_var) in safe_zip(cb_tuple, data_tuple):
orig_var = data_var
data_var = CallbackOp(callback)(data_var)
assert len(data_var.owner.inputs) == 1
assert orig_var is data_var.owner.inputs[0]
costs.append(data_var.sum())
# sum() will call theano.add on the symbolic variables
cost = sum(costs)
model_terms = sum([param.sum() for param in model.get_params()])
cost = cost * model_terms
return cost
def _build_data_specs(self):
"""
Computes a nested data_specs for input and all channels
Also computes the mapping to flatten it. This function is
called from redo_theano.
"""
# Ask the model what it needs
m_space, m_source = self.model.get_monitoring_data_specs()
input_spaces = [m_space]
input_sources = [m_source]
for channel in self.channels.values():
space = channel.data_specs[0]
assert isinstance(space, Space)
input_spaces.append(space)
input_sources.append(channel.data_specs[1])
nested_space = CompositeSpace(input_spaces)
nested_source = tuple(input_sources)
self._nested_data_specs = (nested_space, nested_source)
self._data_specs_mapping = DataSpecsMapping(self._nested_data_specs)
flat_space = self._data_specs_mapping.flatten(nested_space,
return_tuple=True)
flat_source = self._data_specs_mapping.flatten(nested_source,
return_tuple=True)
self._flat_data_specs = (CompositeSpace(flat_space), flat_source)
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 test_nest_specs():
x1 = TT.matrix("x1")
x2 = TT.matrix("x2")
x3 = TT.matrix("x3")
x4 = TT.matrix("x4")
for nested_space, nested_source, nested_data in [
(VectorSpace(dim=10), "target", x2),
(CompositeSpace([VectorSpace(dim=3), VectorSpace(dim=9)]), ("features", "features"), (x1, x4)),
(
CompositeSpace([VectorSpace(dim=3), CompositeSpace([VectorSpace(dim=10), VectorSpace(dim=7)])]),
("features", ("target", "features")),
(x1, (x2, x3)),
),
]:
mapping = DataSpecsMapping((nested_space, nested_source))
flat_space = mapping.flatten(nested_space)
flat_source = mapping.flatten(nested_source)
flat_data = mapping.flatten(nested_data)
renested_space = mapping.nest(flat_space)
renested_source = mapping.nest(flat_source)
renested_data = mapping.nest(flat_data)
assert_equal(renested_space, nested_space)
assert_equal(renested_source, nested_source)
assert_equal(renested_data, nested_data)
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 __init__(self, data_callbacks, data_specs):
"""
data_callback: optional, callbacks to run on data.
It is either a Python callable, or a tuple (possibly nested),
in the same format as data_specs.
data_specs: (space, source) pair specifying the format
and label associated to the data.
"""
self.data_callbacks = data_callbacks
self.data_specs = data_specs
self._mapping = DataSpecsMapping(data_specs)
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_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 test_flatten_specs():
for space, source, flat_space, flat_source in [
# (None, None),
(VectorSpace(dim=5), "features", VectorSpace(dim=5), "features"),
(
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=2)]),
("features", "features"),
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=2)]),
("features", "features"),
),
(
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=5)]),
("features", "targets"),
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=5)]),
("features", "targets"),
),
(
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=5)]),
("features", "features"),
VectorSpace(dim=5),
"features",
),
(
CompositeSpace([VectorSpace(dim=5), CompositeSpace([VectorSpace(dim=9), VectorSpace(dim=12)])]),
("features", ("features", "targets")),
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=9), VectorSpace(dim=12)]),
("features", "features", "targets"),
),
(
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=9), VectorSpace(dim=12)]),
("features", "features", "targets"),
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=9), VectorSpace(dim=12)]),
("features", "features", "targets"),
),
]:
mapping = DataSpecsMapping((space, source))
rval = (mapping.flatten(space), mapping.flatten(source))
assert_equal((flat_space, flat_source), rval)
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 setup(self, model, dataset, algorithm):
self.origin = model.get_param_vector()
cost = algorithm.cost
# Cargo cult all the Pascal bullshit needed to evaluate the f*****g cost function now
# =======================================
data_specs = cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
# End cargo culting
# ======================
print "Compiling cost function..."
cost_fn = function(theano_args, cost_value)
self.cost_fn = cost_fn
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 test_flatten_specs():
for space, source, flat_space, flat_source in [
#(None, None),
(VectorSpace(dim=5), 'features', VectorSpace(dim=5), 'features'),
(CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=2)]),
('features', 'features'),
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=2)]),
('features', 'features')),
(CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=5)]),
('features', 'targets'),
CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=5)]),
('features', 'targets')),
(CompositeSpace([VectorSpace(dim=5), VectorSpace(dim=5)]),
('features', 'features'),
VectorSpace(dim=5),
'features'),
(CompositeSpace([VectorSpace(dim=5),
CompositeSpace([VectorSpace(dim=9),
VectorSpace(dim=12)])]),
('features', ('features', 'targets')),
CompositeSpace([VectorSpace(dim=5),
VectorSpace(dim=9),
VectorSpace(dim=12)]),
('features', 'features', 'targets')),
(CompositeSpace([VectorSpace(dim=5),
VectorSpace(dim=9),
VectorSpace(dim=12)]),
('features', 'features', 'targets'),
CompositeSpace([VectorSpace(dim=5),
VectorSpace(dim=9),
VectorSpace(dim=12)]),
('features', 'features', 'targets'))
]:
mapping = DataSpecsMapping((space, source))
rval = (mapping.flatten(space), mapping.flatten(source))
assert_equal((flat_space, flat_source), rval)
def get_composite_specs_and_mapping(self, model):
"""
Build the composite data_specs and a mapping to flatten it, return both
Build the composite data_specs described in `get_composite_specs`,
and build a DataSpecsMapping that can convert between it and a flat
equivalent version. In particular, it helps building a flat data_specs
to request data, and nesting this data back to the composite data_specs,
so it can be dispatched among the different sub-costs.
This is a helper function used by `get_data_specs` and `get_gradients`,
and possibly other methods.
"""
composite_space, sources = self.get_composite_data_specs(model)
mapping = DataSpecsMapping((composite_space, sources))
return (composite_space, sources), mapping
def setup(self,
dataset,
cost,
batch_size,
num_batches=None,
extra_costs=None,
mode='sequential',
obj_prereqs=None,
cost_monitoring_args=None):
if dataset is None:
return
if isinstance(dataset, Dataset):
dataset = {'': dataset}
else:
assert isinstance(dataset, dict)
assert all(isinstance(key, str) for key in dataset)
assert all(isinstance(dataset[key], Dataset) for key in dataset)
if extra_costs is None:
costs = {}
else:
assert isinstance(extra_costs, (OrderedDict, dict))
costs = extra_costs
assert '' not in costs
costs[''] = cost
if cost_monitoring_args is None:
cost_monitoring_args = {}
model = self.model
# Build a composite data_specs containing the specs for all costs,
# then the specs of the model
cost_names = sorted(costs.keys())
spaces = []
sources = []
for c in cost_names:
c_space, c_source = costs[c].get_data_specs(model)
spaces.append(c_space)
sources.append(c_source)
# Ask the model for the data_specs needed
m_space, m_source = model.get_monitoring_data_specs()
spaces.append(m_space)
sources.append(m_source)
nested_space = CompositeSpace(spaces)
nested_sources = tuple(sources)
# Flatten this data_specs, so we build only one symbolic Theano
# variable for each of the unique (space, source) pairs.
mapping = DataSpecsMapping((nested_space, nested_sources))
space_tuple = mapping.flatten(nested_space, return_tuple=True)
source_tuple = mapping.flatten(nested_sources, return_tuple=True)
ipt = tuple(
space.make_theano_batch(name='monitor_%s' % source,
batch_size=None)
for (space, source) in safe_zip(space_tuple, source_tuple))
# Build a nested tuple from ipt, to dispatch the appropriate parts
# of the ipt batch to each cost
nested_ipt = mapping.nest(ipt)
# custom_channels = {}
# for i, cost_name in enumerate(cost_names):
# if cost_name == '':
# prefix = ''
# else:
# prefix = cost_name + '_'
# cost = costs[cost_name]
# cost_ipt = nested_ipt[i]
# raw_channels = cost.get_monitoring_channels(model, cost_ipt)
# channels = {}
# for name in raw_channels:
# # We need three things: the value itself (raw_channels[name]),
# # the input variables (cost_ipt), and the data_specs for
# # these input variables ((spaces[i], sources[i]))
# channels[prefix + name] = (raw_channels[name],
# cost_ipt,
# (spaces[i], sources[i]))
# custom_channels.update(channels)
#
# # Use the last inputs from nested_ipt for the model
# model_channels = model.get_monitoring_channels(nested_ipt[-1])
# channels = {}
# for name in model_channels:
# # Note: some code used to consider that model_channels[name]
# # could be a a (channel, prereqs) pair, this is not supported.
# channels[name] = (model_channels[name],
# nested_ipt[-1],
# (spaces[-1], sources[-1]))
# custom_channels.update(channels)
if is_stochastic(mode):
seed = [[2013, 2, 22]]
else:
seed = None
for dataset_name in dataset:
cur_dataset = dataset[dataset_name]
self.add_dataset(dataset=cur_dataset,
mode=mode,
batch_size=batch_size,
num_batches=num_batches,
seed=seed)
if dataset_name == '':
dprefix = ''
else:
dprefix = dataset_name + '_'
# These channel name 'objective' must not vary, since callbacks
# that respond to the values in the monitor use the name to find
# it.
for i, cost_name in enumerate(cost_names):
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
cost_value_list = cost.expr(model, cost_ipt)
cost_value = reduce(lambda x, y: x + y, cost_value_list)
if cost_value is not None:
if cost_name == '':
name = dprefix + 'objective'
prereqs = obj_prereqs
else:
name = dprefix + cost_name
prereqs = None
cost.get_data_specs(model)[0].validate(cost_ipt)
self.add_channel(name=name,
ipt=cost_ipt,
val=cost_value,
data_specs=cost.get_data_specs(model),
dataset=cur_dataset,
prereqs=prereqs)
def agent_train(self, terminal):
"""
Training function.
terminal: boolean
Whether current state is a terminal state.
"""
# Wait until we have enough data to train
if self.action_count >= ((self.train.algorithm.batch_size+1)*self.k+1):
tic = time()
if self.train_setup == 0:
self.train.main_loop()
data_specs = self.train.algorithm.cost.get_data_specs(
self.model)
# The iterator should be built from flat data specs, so it
# returns flat, non-redundent tuples of data.
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(
data_specs[1],
return_tuple=True
)
if len(space_tuple) == 0:
# No data will be returned by the iterator, and it is
# impossible to know the size of the actual batch. It
# is not decided yet what the right thing to do should be.
raise NotImplementedError(
"Unable to train with SGD, because the cost does not"
" actually use data from the data set. "
"data_specs: %s" % str(data_specs)
)
flat_data_specs = (CompositeSpace(space_tuple), source_tuple)
self.flat_data_specs = flat_data_specs
self.train_setup = 1
else:
tic_iter = time()
temp_iter = self.train.dataset.iterator(
mode=self.train.algorithm.train_iteration_mode,
batch_size=self.train.algorithm.batch_size,
data_specs=self.flat_data_specs,
return_tuple=True,
rng=self.train.algorithm.rng,
num_batches=self.train.algorithm.batches_per_iter
)
toc_iter = time()
log.debug('Iter creation time: %0.2f' % (toc_iter - tic_iter))
tic_next = time()
batch = temp_iter.next()
toc_next = time()
log.debug('Iter next time: %0.2f' % (toc_next - tic_next))
tic_sgd = time()
self.train.algorithm.sgd_update(*batch)
toc_sgd = time()
log.debug('SGD time: %0.2f' % (toc_sgd - tic_sgd))
log.info('Frames seen: %d' % self.all_time_total_frames)
log.info('Epsilon: %0.10f' % self.epsilon)
toc = time()
self.episode_training_time += toc-tic
log.debug('Real train time: %0.2f' % (toc-tic))
def setup(self, model, dataset):
"""
Compiles the theano functions needed for the train method.
Parameters
----------
model : a Model instance
dataset : Dataset
"""
if self.cost is None:
self.cost = model.get_default_cost()
inf_params = [param for param in model.get_params()
if contains_inf(param.get_value())]
if len(inf_params) > 0:
raise ValueError("These params are Inf: "+str(inf_params))
if any([contains_nan(param.get_value())
for param in model.get_params()]):
nan_params = [param for param in model.get_params()
if contains_nan(param.get_value())]
raise ValueError("These params are NaN: "+str(nan_params))
self.model = model
self._synchronize_batch_size(model)
model._test_batch_size = self.batch_size
self.monitor = Monitor.get_monitor(model)
self.monitor._sanity_check()
# test if force batch size and batch size
has_force_batch_size = getattr(model, "force_batch_size", False)
train_dataset_is_uneven = \
dataset.get_num_examples() % self.batch_size != 0
has_monitoring_datasets = \
self.monitoring_dataset is not None and \
self.monitoring_dataset.values() > 0
if has_monitoring_datasets:
monitoring_datasets_are_uneven = \
any(d.get_num_examples() % self.batch_size
!= 0 for d in self.monitoring_dataset.values())
else:
monitoring_datasets_are_uneven = False # or True it doesn't matter
if has_force_batch_size and train_dataset_is_uneven and \
not has_uniform_batch_size(self.train_iteration_mode):
raise ValueError("Dataset size is not a multiple of batch size."
"You should set train_iteration_mode (and "
"maybe monitor_iteration_mode) to "
"even_sequential, even_shuffled_sequential or "
"even_batchwise_shuffled_sequential")
if has_force_batch_size and has_monitoring_datasets and \
monitoring_datasets_are_uneven and \
not has_uniform_batch_size(self.monitor_iteration_mode):
raise ValueError("Dataset size is not a multiple of batch size."
"You should set monitor_iteration_mode to "
"even_sequential, even_shuffled_sequential or "
"even_batchwise_shuffled_sequential")
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
if cost_value is not None and cost_value.name is None:
# Concatenate the name of all tensors in theano_args !?
cost_value.name = 'objective'
learning_rate = self.learning_rate
params = list(model.get_params())
assert len(params) > 0
for i, param in enumerate(params):
if param.name is None:
param.name = 'sgd_params[%d]' % i
grads, updates = self.cost.get_gradients(model, nested_args,
** fixed_var_descr.fixed_vars)
if not isinstance(grads, OrderedDict):
raise TypeError(str(type(self.cost)) + ".get_gradients returned " +
"something with" + str(type(grads)) + "as its " +
"first member. Expected OrderedDict.")
for param in grads:
assert param in params
for param in params:
assert param in grads
for param in grads:
if grads[param].name is None and cost_value is not None:
grads[param].name = ('grad(%(costname)s, %(paramname)s)' %
{'costname': cost_value.name,
'paramname': param.name})
assert grads[param].dtype == param.dtype
lr_scalers = model.get_lr_scalers()
for key in lr_scalers:
if key not in params:
raise ValueError("Tried to scale the learning rate on " +\
str(key)+" which is not an optimization parameter.")
log.info('Parameter and initial learning rate summary:')
for param in params:
param_name = param.name
if param_name is None:
param_name = 'anon_param'
lr = learning_rate.get_value() * lr_scalers.get(param,1.)
log.info('\t' + param_name + ': ' + str(lr))
if self.learning_rule:
updates.update(self.learning_rule.get_updates(
learning_rate, grads, lr_scalers))
else:
# Use standard SGD updates with fixed learning rate.
updates.update( dict(safe_zip(params, [param - learning_rate * \
lr_scalers.get(param, 1.) * grads[param]
for param in params])))
for param in params:
if updates[param].name is None:
updates[param].name = 'sgd_update(' + param.name + ')'
model.modify_updates(updates)
for param in params:
update = updates[param]
if update.name is None:
update.name = 'censor(sgd_update(' + param.name + '))'
for update_val in get_debug_values(update):
if contains_inf(update_val):
raise ValueError("debug value of %s contains infs" %
update.name)
if contains_nan(update_val):
raise ValueError("debug value of %s contains nans" %
update.name)
# Set up monitor to model the objective value, learning rate,
# momentum (if applicable), and extra channels defined by
# the cost.
# We have to do that after learning_rule.get_updates has been
# called, since it may have an effect on
# learning_rule.add_channels_to_monitor (that is currently the case
# for AdaDelta and RMSProp).
self._setup_monitor()
with log_timing(log, 'Compiling sgd_update'):
self.sgd_update = function(theano_args,
updates=updates,
name='sgd_update',
on_unused_input='ignore',
mode=self.theano_function_mode)
self.params = params
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 : object
A Python object representing the model to train. Loosely
implementing the interface of models.model.Model.
dataset : pylearn2.datasets.dataset.Dataset
Dataset object used to draw training data
"""
self.model = model
if self.cost is None:
self.cost = model.get_default_cost()
try:
if self.cost.is_stochastic():
raise TypeError("BGD is not compatible with stochastic "
"costs.")
except NotImplementedError:
warnings.warn("BGD is not compatible with stochastic costs "
"and cannot determine whether the current cost is "
"stochastic.")
if self.batch_size is None:
self.batch_size = model.force_batch_size
else:
batch_size = self.batch_size
if self.set_batch_size:
model.set_batch_size(batch_size)
elif hasattr(model, 'force_batch_size'):
if not (model.force_batch_size is None or
model.force_batch_size <= 0 or
batch_size == model.force_batch_size):
raise ValueError("batch_size is %d but " +
"model.force_batch_size is %d" %
(batch_size, model.force_batch_size))
self.monitor = Monitor.get_monitor(model)
self.monitor.set_theano_function_mode(self.theano_function_mode)
data_specs = self.cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space,
# named according to the sources.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = 'BGD_[%s]' % source
arg = space.make_theano_batch(name=name)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with their data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
grads, grad_updates = self.cost.get_gradients(
model, nested_args, ** fixed_var_descr.fixed_vars)
assert isinstance(grads, OrderedDict)
assert isinstance(grad_updates, OrderedDict)
if cost_value is None:
raise ValueError("BGD is incompatible with " + str(self.cost) +
" because it is intractable, but BGD uses the " +
"cost function value to do line searches.")
# obj_prereqs has to be a list of function f called with f(*data),
# where data is a data tuple coming from the iterator.
# this function enables capturing "mapping" and "f", while
# enabling the "*data" syntax
def capture(f, mapping=mapping):
new_f = lambda *args: f(mapping.flatten(args, return_tuple=True))
return new_f
obj_prereqs = [capture(f) for f in fixed_var_descr.on_load_batch]
if self.monitoring_dataset is not None:
if (self.monitoring_batch_size is None and
self.monitoring_batches is None):
self.monitoring_batch_size = self.batch_size
self.monitoring_batches = self.batches_per_iter
self.monitor.setup(
dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.monitoring_batch_size,
num_batches=self.monitoring_batches,
obj_prereqs=obj_prereqs,
cost_monitoring_args=fixed_var_descr.fixed_vars)
params = model.get_params()
self.optimizer = BatchGradientDescent(
objective=cost_value,
gradients=grads,
gradient_updates=grad_updates,
params=params,
param_constrainers=[model.modify_updates],
lr_scalers=model.get_lr_scalers(),
inputs=theano_args,
verbose=self.verbose_optimization,
max_iter=self.updates_per_batch,
reset_alpha=self.reset_alpha,
conjugate=self.conjugate,
reset_conjugate=self.reset_conjugate,
min_init_alpha=self.min_init_alpha,
line_search_mode=self.line_search_mode,
theano_function_mode=self.theano_function_mode,
init_alpha=self.init_alpha)
# These monitoring channels keep track of shared variables,
# which do not need inputs nor data.
if self.monitoring_dataset is not None:
self.monitor.add_channel(
name='ave_step_size',
ipt=None,
val=self.optimizer.ave_step_size,
data_specs=(NullSpace(), ''),
dataset=first_value(self.monitoring_dataset))
self.monitor.add_channel(
name='ave_grad_size',
ipt=None,
val=self.optimizer.ave_grad_size,
data_specs=(NullSpace(), ''),
dataset=first_value(self.monitoring_dataset))
self.monitor.add_channel(
name='ave_grad_mult',
ipt=None,
val=self.optimizer.ave_grad_mult,
data_specs=(NullSpace(), ''),
dataset=first_value(self.monitoring_dataset))
self.first = True
self.bSetup = True
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 : object
A Python object representing the model to train loosely \
implementing the interface of models.model.Model.
dataset : pylearn2.datasets.dataset.Dataset
Dataset object used to draw training data
"""
self.model = model
if self.cost is None:
self.cost = model.get_default_cost()
if self.batch_size is None:
self.batch_size = model.force_batch_size
else:
batch_size = self.batch_size
if self.set_batch_size:
model.set_batch_size(batch_size)
elif hasattr(model, 'force_batch_size'):
if not (model.force_batch_size <= 0
or batch_size == model.force_batch_size):
raise ValueError("batch_size is %d but " +
"model.force_batch_size is %d" %
(batch_size, model.force_batch_size))
self.monitor = Monitor.get_monitor(model)
self.monitor.set_theano_function_mode(self.theano_function_mode)
data_specs = self.cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space,
# named according to the sources.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = 'BGD_[%s]' % source
arg = space.make_theano_batch(name=name)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with their data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
**fixed_var_descr.fixed_vars)
grads, grad_updates = self.cost.get_gradients(
model, nested_args, **fixed_var_descr.fixed_vars)
assert isinstance(grads, OrderedDict)
assert isinstance(grad_updates, OrderedDict)
if cost_value is None:
raise ValueError("BGD is incompatible with " + str(self.cost) +
" because it is intractable, but BGD uses the " +
"cost function value to do line searches.")
# obj_prereqs has to be a list of function f called with f(*data),
# where data is a data tuple coming from the iterator.
# this function enables capturing "mapping" and "f", while
# enabling the "*data" syntax
def capture(f, mapping=mapping):
new_f = lambda *args: f(mapping.flatten(args, return_tuple=True))
return new_f
obj_prereqs = [capture(f) for f in fixed_var_descr.on_load_batch]
if self.monitoring_dataset is not None:
if (self.monitoring_batch_size is None
and self.monitoring_batches is None):
self.monitoring_batch_size = self.batch_size
self.monitoring_batches = self.batches_per_iter
self.monitor.setup(dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.monitoring_batch_size,
num_batches=self.monitoring_batches,
obj_prereqs=obj_prereqs,
cost_monitoring_args=fixed_var_descr.fixed_vars)
params = model.get_params()
self.optimizer = BatchGradientDescent(
objective=cost_value,
gradients=grads,
gradient_updates=grad_updates,
params=params,
param_constrainers=[model.censor_updates],
lr_scalers=model.get_lr_scalers(),
inputs=theano_args,
verbose=self.verbose_optimization,
max_iter=self.updates_per_batch,
reset_alpha=self.reset_alpha,
conjugate=self.conjugate,
reset_conjugate=self.reset_conjugate,
min_init_alpha=self.min_init_alpha,
line_search_mode=self.line_search_mode,
theano_function_mode=self.theano_function_mode,
init_alpha=self.init_alpha)
# These monitoring channels keep track of shared variables,
# which do not need inputs nor data.
if self.monitoring_dataset is not None:
self.monitor.add_channel(
name='ave_step_size',
ipt=None,
val=self.optimizer.ave_step_size,
data_specs=(NullSpace(), ''),
dataset=self.monitoring_dataset.values()[0])
self.monitor.add_channel(
name='ave_grad_size',
ipt=None,
val=self.optimizer.ave_grad_size,
data_specs=(NullSpace(), ''),
dataset=self.monitoring_dataset.values()[0])
self.monitor.add_channel(
name='ave_grad_mult',
ipt=None,
val=self.optimizer.ave_grad_mult,
data_specs=(NullSpace(), ''),
dataset=self.monitoring_dataset.values()[0])
self.first = True
self.bSetup = True
def setup(self,
dataset,
cost,
batch_size,
num_batches=None,
extra_costs=None,
mode='sequential',
obj_prereqs=None,
cost_monitoring_args=None):
"""
Sets up the monitor for a cost minimization problem.
Adds channels defined by both the model and the cost for
the specified dataset(s), as well as a channel called 'objective'
defined by the costs' __call__ method.
Parameters
----------
dataset : pylearn2.datasets.Dataset
Dataset or dictionary mapping string names to Datasets. If \
string names are used, then for every dataset, each channel \
defined by the model or cost will be replicated with that \
dataset's name followed by an underscore as the prefix. For \
example, if your cost defines a channel called 'misclass', and \
datasets is {'train' : train_dataset, 'valid' : valid_dataset} \
you will get channels called 'train_misclass' and 'valid_misclass'.
cost : pylearn2.costs.Cost
The cost being optimized by training. The value of the cost will
appear as the `objective` channel. Its `get_monitoring_channels`
method will also be used to supply other channels.
extra_costs : OrderedDict, optional
A dictionary mapping channel names to Cost objects.
Their value will appear as the specified channel name.
They will also provide more monitoring channels via their
`get_monitoring_channels` method.
obj_prereqs : None, or list of functions
Functions to pass as prerequisites to the `objective` channel.
cost_monitoring_args : dict
Dictionary of kwargs that will be passed to \
`cost.get_monitoring_channels()` (but not for the extra_costs).
"""
if dataset is None:
return
if isinstance(dataset, Dataset):
dataset = {'': dataset}
else:
assert isinstance(dataset, dict)
assert all(isinstance(key, str) for key in dataset)
assert all(isinstance(dataset[key], Dataset) for key in dataset)
if extra_costs is None:
costs = {}
else:
costs = extra_costs
assert '' not in costs
costs[''] = cost
if cost_monitoring_args is None:
cost_monitoring_args = {}
model = self.model
# Build a composite data_specs containing the specs for all costs,
# then the specs of the model
cost_names = sorted(costs.keys())
spaces = []
sources = []
for c in cost_names:
c_space, c_source = costs[c].get_data_specs(model)
spaces.append(c_space)
sources.append(c_source)
# Ask the model for the data_specs needed
m_space, m_source = model.get_monitoring_data_specs()
spaces.append(m_space)
sources.append(m_source)
nested_space = CompositeSpace(spaces)
nested_sources = tuple(sources)
# Flatten this data_specs, so we build only one symbolic Theano
# variable for each of the unique (space, source) pairs.
mapping = DataSpecsMapping((nested_space, nested_sources))
space_tuple = mapping.flatten(nested_space, return_tuple=True)
source_tuple = mapping.flatten(nested_sources, return_tuple=True)
ipt = tuple(
space.make_theano_batch(name='monitor_%s' % source,
batch_size=None)
for (space, source) in safe_zip(space_tuple, source_tuple))
# Build a nested tuple from ipt, to dispatch the appropriate parts
# of the ipt batch to each cost
nested_ipt = mapping.nest(ipt)
custom_channels = {}
for i, cost_name in enumerate(cost_names):
if cost_name == '':
prefix = ''
else:
prefix = cost_name + '_'
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
raw_channels = cost.get_monitoring_channels(model, cost_ipt)
channels = {}
for name in raw_channels:
# We need three things: the value itself (raw_channels[name]),
# the input variables (cost_ipt), and the data_specs for
# these input variables ((spaces[i], sources[i]))
channels[prefix + name] = (raw_channels[name], cost_ipt,
(spaces[i], sources[i]))
custom_channels.update(channels)
# Use the last inputs from nested_ipt for the model
model_channels = model.get_monitoring_channels(nested_ipt[-1])
channels = {}
for name in model_channels:
# Note: some code used to consider that model_channels[name]
# could be a a (channel, prereqs) pair, this is not supported.
channels[name] = (model_channels[name], nested_ipt[-1],
(spaces[-1], sources[-1]))
custom_channels.update(channels)
if is_stochastic(mode):
seed = [[2013, 02, 22]]
else:
seed = None
for dataset_name in dataset:
cur_dataset = dataset[dataset_name]
self.add_dataset(dataset=cur_dataset,
mode=mode,
batch_size=batch_size,
num_batches=num_batches,
seed=seed)
if dataset_name == '':
dprefix = ''
else:
dprefix = dataset_name + '_'
# These channel name 'objective' must not vary, since callbacks
# that respond to the values in the monitor use the name to find
# it.
for i, cost_name in enumerate(cost_names):
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
cost_value = cost.expr(model, cost_ipt)
if cost_value is not None:
if cost_name == '':
name = dprefix + 'objective'
prereqs = obj_prereqs
else:
name = dprefix + cost_name
prereqs = None
cost.get_data_specs(model)[0].validate(cost_ipt)
self.add_channel(name=name,
ipt=cost_ipt,
val=cost_value,
data_specs=cost.get_data_specs(model),
dataset=cur_dataset,
prereqs=prereqs)
for key in custom_channels:
val, ipt, data_specs = custom_channels[key]
data_specs[0].validate(ipt)
self.add_channel(name=dprefix + key,
ipt=ipt,
val=val,
data_specs=data_specs,
dataset=cur_dataset)
def add_channel(self, name, ipt, val, dataset=None, prereqs=None,
data_specs=None):
"""
Asks the monitor to start tracking a new value. Can be called
even after the monitor is already in use.
Parameters
----------
name : str
The display name in the monitor.
ipt : tensor_like
The symbolic tensor which should be clamped to the data.
(or a list/tuple containing symbolic tensors, following the
data_specs)
val : tensor_like
The value (function of `ipt`) to be tracked.
dataset : pylearn2.datasets.Dataset
Which dataset to compute this channel on
prereqs : list of callables that take a list of numpy tensors
Each prereq must be called exactly once per each new batch
of data drawn *from dataset* before the channel value is
computed if two channels provide a prereq with exactly the
same id, that prereq will only be called once
data_specs : (space, source) pair
Identifies the order, format and semantics of ipt
"""
if isinstance(val, (float, int, long)):
val = np.cast[theano.config.floatX](val)
val = T.as_tensor_variable(val)
if data_specs is None:
warnings.warn("parameter 'data_specs' should be provided when " +
"calling add_channel. We will build a default one.",
stacklevel=2)
if isinstance(ipt, list):
ipt = tuple(ipt)
if ipt is not None and not isinstance(ipt, tuple):
ipt = (ipt,)
if ipt is None:
data_specs = (NullSpace(), '')
elif len(ipt) == 0:
data_specs = (CompositeSpace([]), ())
elif hasattr(dataset, 'get_data_specs'):
dataset_space, dataset_source = dataset.get_data_specs()
if (len(ipt) == 1 and
dataset_source is not None and
(not isinstance(dataset_source, tuple) or
len(dataset_source) == 1) and
'features' in dataset_source):
data_specs = (dataset_space, dataset_source)
elif (len(ipt) == 2 and
dataset_source == ('features', 'targets')):
data_specs = (dataset_space, dataset_source)
else:
raise ValueError("Cannot infer default data_specs for " +
"the following input points and " +
"dataset: ipt = %s, dataset = %s"
% (ipt, dataset))
data_specs[0].validate(ipt)
mapping = DataSpecsMapping(data_specs)
flat_ipt = mapping.flatten(ipt)
if not isinstance(flat_ipt, tuple):
flat_ipt = (flat_ipt,)
inputs = theano.gof.graph.inputs([val])
for elem in inputs:
if not hasattr(elem, 'get_value') and \
not isinstance(elem, theano.gof.graph.Constant):
if elem not in flat_ipt:
raise ValueError("Unspecified input: " + str(elem) +
". This may be due to an incorrect " +
"implementation of a cost's " +
"get_data_specs() method, or of a " +
"model's get_monitoring_data_specs() " +
"method.")
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('Adding monitor channel '+name+'\n')
assert isinstance(flat_ipt, tuple)
if len(flat_ipt) != 1:
for elem in flat_ipt:
mode.record.handle_line('Includes input var ' +
var_descriptor(elem) + '\n')
else:
mode.record.handle_line(name + ' input var is ' +
var_descriptor(flat_ipt[0]) + '\n')
mode.record.handle_line('channel ' + name + ' is ' +
var_descriptor(val) + '\n')
if dataset is None:
if len(self._datasets) == 1:
dataset = self._datasets[0]
elif len(self._datasets) == 0:
raise ValueError(_err_no_data)
else:
raise ValueError(_err_ambig_data)
try:
self._datasets.index(dataset)
except ValueError:
reraise_as(ValueError("The dataset specified is not one of the " +
"monitor's datasets"))
if name in self.channels:
raise ValueError("Tried to create the same channel twice (%s)" %
name)
self.channels[name] = MonitorChannel(ipt, val, name, data_specs,
dataset, prereqs)
self._dirty = True
class Monitor(object):
"""
A class for monitoring Models while they are being trained.
A monitor object records the number of minibatches and number of examples
the model has trained, as well as any number of "channels" that track
quantities of interest (examples: the objective function, measures of
hidden unit activity, reconstruction error, sum of squared second
derivatives, average norm of the weight vectors, etc.)
"""
def __init__(self, model):
"""
Makes a monitor for `model`. Assumes the model has not been trained at
all yet.
Parameters
----------
model : pylearn2.models.model.Model instance
"""
self.training_succeeded = False
self.model = model
self.channels = OrderedDict()
self._num_batches_seen = 0
self._examples_seen = 0
self._epochs_seen = 0
self._datasets = []
self._iteration_mode = []
self._batch_size = []
self._num_batches = []
self._dirty = True
self._rng_seed = []
self.names_to_del = ['theano_function_mode']
self.t0 = time.time()
self.theano_function_mode = None
# Initialize self._nested_data_specs, self._data_specs_mapping,
# and self._flat_data_specs
self._build_data_specs()
def _build_data_specs(self):
"""
Computes a nested data_specs for input and all channels
Also computes the mapping to flatten it. This function is called from
redo_theano.
"""
# Ask the model what it needs
m_space, m_source = self.model.get_monitoring_data_specs()
input_spaces = [m_space]
input_sources = [m_source]
for channel in self.channels.values():
space = channel.data_specs[0]
assert isinstance(space, Space)
input_spaces.append(space)
input_sources.append(channel.data_specs[1])
nested_space = CompositeSpace(input_spaces)
nested_source = tuple(input_sources)
self._nested_data_specs = (nested_space, nested_source)
self._data_specs_mapping = DataSpecsMapping(self._nested_data_specs)
flat_space = self._data_specs_mapping.flatten(nested_space,
return_tuple=True)
flat_source = self._data_specs_mapping.flatten(nested_source,
return_tuple=True)
self._flat_data_specs = (CompositeSpace(flat_space), flat_source)
def set_theano_function_mode(self, mode):
"""
Parameters
----------
mode : theano.compile.Mode
Theano functions for the monitoring channels will be compiled and
run using this mode.
"""
if self.theano_function_mode != mode:
self._dirty = True
self.theano_function_mode = mode
def add_dataset(self,
dataset,
mode='sequential',
batch_size=None,
num_batches=None,
seed=None):
"""
Determines the data used to calculate the values of each channel.
Parameters
----------
dataset : object
A `pylearn2.datasets.Dataset` object.
mode : str or object, optional
Iteration mode; see the docstring of the `iterator` method \
on `pylearn2.datasets.Dataset` for details.
batch_size : int, optional
The size of an individual batch. Optional if `mode` is \
'sequential' and `num_batches` is specified (batch size \
will be calculated based on full dataset size).
num_batches : int, optional
The total number of batches. Unnecessary if `mode` is \
'sequential' and `batch_size` is specified (number of \
batches will be calculated based on full dataset size).
seed : int, optional
Optional. The seed to be used for random iteration modes.
"""
# The user can ommit using lists if only one dataset is set
if not isinstance(dataset, list):
dataset = [dataset]
if not isinstance(mode, list):
mode = [mode]
if not isinstance(batch_size, list):
batch_size = [batch_size]
if not isinstance(num_batches, list):
num_batches = [num_batches]
if seed is None:
seed = [None] * len(dataset)
if not isinstance(seed, list):
seed = [seed]
if len(mode) != len(dataset):
raise ValueError("Received " + str(len(dataset)) +
" dataset but " + str(len(mode)) + " modes.")
if any([len(l) != len(dataset) for l in [batch_size, seed]]):
raise ValueError("make sure each dataset has its iteration " +
"batch size and number of batches.")
for (d, m, b, n, sd) in safe_izip(dataset, mode, batch_size,
num_batches, seed):
try:
it = d.iterator(mode=m,
batch_size=b,
num_batches=n,
data_specs=self._flat_data_specs,
return_tuple=True,
rng=sd)
except ValueError as exc:
raise ValueError("invalid iteration parameters in " +
"Monitor.add_dataset: " + str(exc))
if it.stochastic:
# Must be a seed, not a random number generator. If it were a
# random number generator, different iterators using it would
# update its state, so we would not get the same iterator
# each time. Also, must not be None, because this makes the
# iterator pick a seed based on the clock
if sd is None:
raise TypeError("Monitor requires a seed when using " +
"stochastic iteration modes.")
if not isinstance(sd, (list, tuple, int)):
raise TypeError("Monitor requires a seed (not a random " +
"number generator) when using " +
"stochastic iteration modes.")
else:
# The iterator should catch this, but let's double-check
assert sd is None
if not d in self._datasets:
self._datasets.append(d)
self._iteration_mode.append(m)
self._batch_size.append(b)
self._num_batches.append(n)
self._rng_seed.append(sd)
def __call__(self):
"""
Runs the model on the monitoring dataset in order to add one data point
to each of the channels.
"""
# If the channels have changed at all, we need to recompile the theano
# functions used to compute them
if self._dirty:
self.redo_theano()
datasets = self._datasets
# Set all channels' val_shared to 0
self.begin_record_entry()
for d, i, b, n, a, sd, ne in safe_izip(datasets, self._iteration_mode,
self._batch_size,
self._num_batches, self.accum,
self._rng_seed,
self.num_examples):
if isinstance(d, basestring):
d = yaml_parse.load(d)
raise NotImplementedError()
# need to put d back into self._datasets
myiterator = d.iterator(mode=i,
batch_size=b,
num_batches=n,
data_specs=self._flat_data_specs,
return_tuple=True,
rng=sd)
# If self._flat_data_specs is empty, no channel needs data,
# so we do not need to call the iterator in order to average
# the monitored values across different batches, we only
# have to call them once.
if len(self._flat_data_specs[1]) == 0:
X = ()
self.run_prereqs(X, d)
a(*X)
else:
actual_ne = 0
for X in myiterator:
# X is a flat (not nested) tuple
self.run_prereqs(X, d)
a(*X)
actual_ne += self._flat_data_specs[0].np_batch_size(X)
# end for X
if actual_ne != ne:
raise RuntimeError("At compile time, your iterator said "
"it had %d examples total, but at "
"runtime it gave us %d." %
(ne, actual_ne))
# end for d
log.info("Monitoring step:")
log.info("\tEpochs seen: %d" % self._epochs_seen)
log.info("\tBatches seen: %d" % self._num_batches_seen)
log.info("\tExamples seen: %d" % self._examples_seen)
t = time.time() - self.t0
for channel_name in sorted(self.channels.keys(),
key=number_aware_alphabetical_key):
channel = self.channels[channel_name]
channel.time_record.append(t)
channel.batch_record.append(self._num_batches_seen)
channel.example_record.append(self._examples_seen)
channel.epoch_record.append(self._epochs_seen)
val = channel.val_shared.get_value()
channel.val_record.append(val)
# TODO: use logging infrastructure so that user can configure
# formatting
if abs(val) < 1e4:
val_str = str(val)
else:
val_str = '%.3e' % val
log.info("\t%s: %s" % (channel_name, val_str))
def run_prereqs(self, data, dataset):
"""
Runs all "prerequistie functions" on a batch of data. Always called
right before computing the monitoring channels on that batch.
Parameters
----------
data : tuple or Variable
a member of the Space used as input to the monitoring functions
dataset : Dataset
the Dataset the data was drawn from
"""
if dataset not in self.prereqs:
return
for prereq in self.prereqs[dataset]:
prereq(*data)
def get_batches_seen(self):
"""
Returns the number of batches the model has learned on (assuming that
the learning code has been calling Monitor.report_batch correctly).
"""
return self._num_batches_seen
def get_epochs_seen(self):
"""
Returns
-------
epochs_seen : int
The number of epochs the model has been trained on.
One "epoch" is one pass through Dataset.iterator.
"""
return self._epochs_seen
def get_examples_seen(self):
"""
Returns
-------
examples_seen : int
The number of examples the model has learned on (assuming that
the learning code has been calling Monitor.report_batch correctly)
"""
return self._examples_seen
def report_batch(self, num_examples):
"""
Call this whenever the model has learned on another batch of examples.
Report how many examples were learned on.
Parameters
----------
num_examples : int
The number of examples learned on in this minibatch.
"""
self._examples_seen += num_examples
self._num_batches_seen += 1
def report_epoch(self):
"""
Call this whenever the model has completed another "epoch" of learning.
We regard one pass through Dataset.iterator as one epoch.
"""
self._epochs_seen += 1
def redo_theano(self):
"""
Recompiles Theano functions used by this monitor.
This is called any time we need to evaluate the channels and the
channel definitions have changed since last we called it, or if the
theano functions are unavailable for any other reason (first time they
are needed after construction or deserialization, etc.)
All channels are compiled as part of the same theano function so that
the theano optimizations can eliminate subexpressions that are shared
between multiple channels.
"""
self._dirty = False
# Recompute the data specs, since the channels may have changed.
self._build_data_specs()
init_names = dir(self)
self.prereqs = OrderedDict()
for channel in self.channels.values():
if channel.prereqs is not None:
dataset = channel.dataset
if dataset not in self.prereqs:
self.prereqs[dataset] = []
prereqs = self.prereqs[dataset]
for prereq in channel.prereqs:
if prereq not in prereqs:
prereqs.append(prereq)
updates = OrderedDict()
for channel in self.channels.values():
updates[channel.val_shared] = np.cast[config.floatX](0.0)
with log_timing(log, "compiling begin_record_entry"):
self.begin_record_entry = function(
inputs=[],
updates=updates,
mode=self.theano_function_mode,
name='Monitor.begin_record_entry')
updates = OrderedDict()
givens = OrderedDict()
# Get the appropriate kind of theano variable to represent the data
# the model acts on
batch_names = ['monitoring_%s' % s for s in self._flat_data_specs[1]]
theano_args = self._flat_data_specs[0].make_theano_batch(batch_names)
# Get a symbolic expression of the batch size
# We do it here, rather than for each channel, because channels with an
# empty data_specs do not use data, and are unable to extract the batch
# size. The case where the whole data specs is empty is not supported.
batch_size = self._flat_data_specs[0].batch_size(theano_args)
# Also get a nested representation, for joint iteration
# with each of channel.graph_input
nested_theano_args = self._data_specs_mapping.nest(theano_args)
if not isinstance(nested_theano_args, tuple):
nested_theano_args = (nested_theano_args, )
assert len(nested_theano_args) == (len(self.channels) + 1)
log.info('Monitored channels: ')
for key in sorted(self.channels.keys()):
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling monitor including ' +
'channel ' + key + '\n')
log.info('\t%s' % key)
it = [
d.iterator(mode=i,
num_batches=n,
batch_size=b,
data_specs=self._flat_data_specs,
return_tuple=True)
for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
self._num_batches, self._batch_size)
]
self.num_examples = [
np.cast[config.floatX](float(i.num_examples)) for i in it
]
givens = [OrderedDict() for d in self._datasets]
updates = [OrderedDict() for d in self._datasets]
for i, channel in enumerate(self.channels.values()):
index = self._datasets.index(channel.dataset)
d = self._datasets[index]
g = givens[index]
cur_num_examples = self.num_examples[index]
u = updates[index]
# Flatten channel.graph_input and the appropriate part of
# nested_theano_args, to iterate jointly over them.
c_mapping = DataSpecsMapping(channel.data_specs)
channel_inputs = c_mapping.flatten(channel.graph_input,
return_tuple=True)
inputs = c_mapping.flatten(nested_theano_args[i + 1],
return_tuple=True)
for (channel_X, X) in safe_izip(channel_inputs, inputs):
assert channel_X not in g or g[channel_X] is X
assert channel_X.type == X.type, (channel_X.type, X.type)
g[channel_X] = X
if batch_size == 0:
# No channel does need any data, so there is not need to
# average results, and we will call the accum functions only
# once.
# TODO: better handling of channels not needing data when
# some other channels need data.
assert len(self._flat_data_specs[1]) == 0
val = channel.val
else:
if n == 0:
raise ValueError("Iterating over 0 examples results in " +
"divide by 0")
val = (channel.val * T.cast(batch_size, config.floatX) /
cur_num_examples)
u[channel.val_shared] = channel.val_shared + val
with log_timing(log, "Compiling accum"):
# Check type of update expressions
for up in updates:
for key in up:
if key.dtype != up[key].dtype:
raise TypeError('Monitoring channel shared variable ' +
key.name + ' has dtype ' + key.dtype +
' but is driven by an expression ' +
'with type ' + up[key].dtype)
self.accum = []
for idx, packed in enumerate(safe_izip(givens, updates)):
g, u = packed
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
for elem in g:
mode.record.handle_line('g key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('g val ' +
var_descriptor(g[elem]) + '\n')
for elem in u:
mode.record.handle_line('u key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('u val ' +
var_descriptor(u[elem]) + '\n')
function_name = 'Monitor.accum[%d]' % idx
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling supervised accum\n')
# Some channels may not depend on the data, ie, they might just
# monitor the model parameters, or some shared variable updated
# by the training algorithm, so we need to ignore the unused
# input error
self.accum.append(
function(theano_args,
givens=g,
updates=u,
mode=self.theano_function_mode,
name=function_name))
for a in self.accum:
if mode is not None and hasattr(mode, 'record'):
for elem in a.maker.fgraph.outputs:
mode.record.handle_line('accum output ' +
var_descriptor(elem) + '\n')
log.info("graph size: %d" % len(a.maker.fgraph.toposort()))
final_names = dir(self)
self.register_names_to_del(
[name for name in final_names if name not in init_names])
def register_names_to_del(self, names):
"""
Register names of fields that should be deleted before pickling.
Parameters
----------
names : list
A list of attribute names as strings.
"""
for name in names:
if name not in self.names_to_del:
self.names_to_del.append(name)
def __getstate__(self):
"""
In order to avoid pickling a copy of the dataset whenever a monitor
is saved, the __getstate__ method replaces the dataset field with the
dataset's yaml source. This is not a perfect solution because it won't
work with job resuming, which would require saving the state of the
dataset's random number generator.
Like in the Model class, we also need to avoid saving any Theano
functions, so we delete everything that can be regenerated with
`redo_theano` by deleting the fields in `self.names_to_del`
"""
# Patch old pickled monitors
if not hasattr(self, '_datasets'):
self._datasets = [self._dataset]
del self._dataset
temp = self._datasets
if self._datasets:
self._datasets = []
for dataset in temp:
if isinstance(dataset, basestring):
self._datasets.append(dataset)
else:
try:
self._datasets.append(dataset.yaml_src)
except AttributeError:
warnings.warn('Trained model saved without ' +
'indicating yaml_src')
d = copy.copy(self.__dict__)
self._datasets = temp
for name in self.names_to_del:
if name in d:
del d[name]
return d
def __setstate__(self, d):
"""
Sets the object to have the state described by `d`.
Parameters
----------
d : dict
A dictionary mapping string names of fields to values for
these fields.
"""
# patch old pkl files
if '_dataset' in d:
d['_datasets'] = [d['_dataset']]
del d['_dataset']
self.__dict__.update(d)
def add_channel(self,
name,
ipt,
val,
dataset=None,
prereqs=None,
data_specs=None):
"""
Asks the monitor to start tracking a new value. Can be called even
after the monitor is already in use.
Parameters
----------
name : str
The display name in the monitor.
ipt : tensor_like
The symbolic tensor which should be clamped to the data. \
(or a list/tuple containing symbolic tensors, following the \
data_specs)
val : tensor_like
The value (function of `ipt`) to be tracked.
dataset : pylearn2.datasets.Dataset
Which dataset to compute this channel on
prereqs : list of callables that take a list of numpy tensors
Each prereq must be called exactly once per each new batch of \
data drawn *from dataset* before the channel value is computed \
if two channels provide a prereq with exactly the same id, that \
prereq will only be called once
data_specs : (space, source) pair
Identifies the order, format and semantics of ipt
"""
if isinstance(val, (float, int, long)):
val = np.cast[theano.config.floatX](val)
val = T.as_tensor_variable(val)
if data_specs is None:
warnings.warn("parameter 'data_specs' should be provided when " +
"calling add_channel. We will build a default one.",
stacklevel=2)
if isinstance(ipt, list):
ipt = tuple(ipt)
if ipt is not None and not isinstance(ipt, tuple):
ipt = (ipt, )
if ipt is None:
data_specs = (NullSpace(), '')
elif len(ipt) == 0:
data_specs = (CompositeSpace([]), ())
elif hasattr(dataset, 'get_data_specs'):
dataset_space, dataset_source = dataset.get_data_specs()
if (len(ipt) == 1 and dataset_source is not None
and (not isinstance(dataset_source, tuple)
or len(dataset_source) == 1)
and 'features' in dataset_source):
data_specs = (dataset_space, dataset_source)
elif (len(ipt) == 2
and dataset_source == ('features', 'targets')):
data_specs = (dataset_space, dataset_source)
else:
raise ValueError("Cannot infer default data_specs for " +
"the following input points and " +
"dataset: ipt = %s, dataset = %s" %
(ipt, dataset))
data_specs[0].validate(ipt)
mapping = DataSpecsMapping(data_specs)
flat_ipt = mapping.flatten(ipt)
if not isinstance(flat_ipt, tuple):
flat_ipt = (flat_ipt, )
inputs = theano.gof.graph.inputs([val])
for elem in inputs:
if not hasattr(elem, 'get_value') and \
not isinstance(elem, theano.gof.graph.Constant):
if elem not in flat_ipt:
raise ValueError("Unspecified input: " + str(elem) +
". This may be due to an incorrect " +
"implementation of a cost's " +
"get_data_specs() method, or of a " +
"model's get_monitoring_data_specs() " +
"method.")
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('Adding monitor channel ' + name + '\n')
assert isinstance(flat_ipt, tuple)
if len(flat_ipt) != 1:
for elem in flat_ipt:
mode.record.handle_line('Includes input var ' +
var_descriptor(elem) + '\n')
else:
mode.record.handle_line(name + ' input var is ' +
var_descriptor(flat_ipt[0]) + '\n')
mode.record.handle_line('channel ' + name + ' is ' +
var_descriptor(val) + '\n')
if dataset is None:
if len(self._datasets) == 1:
dataset = self._datasets[0]
elif len(self._datasets) == 0:
raise ValueError(_err_no_data)
else:
raise ValueError(_err_ambig_data)
try:
self._datasets.index(dataset)
except ValueError:
raise ValueError("The dataset specified is not one of the " +
"monitor's datasets")
if name in self.channels:
raise ValueError("Tried to create the same channel twice (%s)" %
name)
self.channels[name] = MonitorChannel(ipt, val, name, data_specs,
dataset, prereqs)
self._dirty = True
def _sanity_check(self):
"""
Sometimes we serialize models and then load them somewhere else
but still try to use their Monitor, and the Monitor is in a mangled
state. I've added some calls to _sanity_check to try to catch when
that happens. Not sure what to do for a long term fix. I think it
requires making theano graphs serializable first.
"""
for name in self.channels:
channel = self.channels[name]
assert hasattr(channel, 'prereqs')
@classmethod
def get_monitor(cls, model):
"""
Returns a model's monitor. If the model doesn't have a monitor yet,
installs one and returns that.
Parameters
----------
model : object
An object that implements the `Model` interface specified in \
`pylearn2.models`.
"""
if hasattr(model, 'monitor'):
rval = model.monitor
rval._sanity_check()
else:
rval = Monitor(model)
model.monitor = rval
return rval
# TODO: find out if this method is used anywhere, remove if not.
@property
def batch_size(self):
"""
Returns
-------
batch_size : int
The size of the batches used for monitoring
"""
return self._batch_size
# TODO: find out if this method is used anywhere, remove if not.
@property
def num_batches(self):
"""
Returns
-------
num_batches : int
The number of batches used for monitoring
"""
return self._num_batches
def setup(self,
dataset,
cost,
batch_size,
num_batches=None,
extra_costs=None,
mode='sequential',
obj_prereqs=None,
cost_monitoring_args=None):
"""
Sets up the monitor for a cost minimization problem.
Adds channels defined by both the model and the cost for
the specified dataset(s), as well as a channel called 'objective'
defined by the costs' __call__ method.
Parameters
----------
dataset : pylearn2.datasets.Dataset
Dataset or dictionary mapping string names to Datasets. If \
string names are used, then for every dataset, each channel \
defined by the model or cost will be replicated with that \
dataset's name followed by an underscore as the prefix. For \
example, if your cost defines a channel called 'misclass', and \
datasets is {'train' : train_dataset, 'valid' : valid_dataset} \
you will get channels called 'train_misclass' and 'valid_misclass'.
cost : pylearn2.costs.Cost
The cost being optimized by training. The value of the cost will
appear as the `objective` channel. Its `get_monitoring_channels`
method will also be used to supply other channels.
extra_costs : OrderedDict, optional
A dictionary mapping channel names to Cost objects.
Their value will appear as the specified channel name.
They will also provide more monitoring channels via their
`get_monitoring_channels` method.
obj_prereqs : None, or list of functions
Functions to pass as prerequisites to the `objective` channel.
cost_monitoring_args : dict
Dictionary of kwargs that will be passed to \
`cost.get_monitoring_channels()` (but not for the extra_costs).
"""
if dataset is None:
return
if isinstance(dataset, Dataset):
dataset = {'': dataset}
else:
assert isinstance(dataset, dict)
assert all(isinstance(key, str) for key in dataset)
assert all(isinstance(dataset[key], Dataset) for key in dataset)
if extra_costs is None:
costs = {}
else:
costs = extra_costs
assert '' not in costs
costs[''] = cost
if cost_monitoring_args is None:
cost_monitoring_args = {}
model = self.model
# Build a composite data_specs containing the specs for all costs,
# then the specs of the model
cost_names = sorted(costs.keys())
spaces = []
sources = []
for c in cost_names:
c_space, c_source = costs[c].get_data_specs(model)
spaces.append(c_space)
sources.append(c_source)
# Ask the model for the data_specs needed
m_space, m_source = model.get_monitoring_data_specs()
spaces.append(m_space)
sources.append(m_source)
nested_space = CompositeSpace(spaces)
nested_sources = tuple(sources)
# Flatten this data_specs, so we build only one symbolic Theano
# variable for each of the unique (space, source) pairs.
mapping = DataSpecsMapping((nested_space, nested_sources))
space_tuple = mapping.flatten(nested_space, return_tuple=True)
source_tuple = mapping.flatten(nested_sources, return_tuple=True)
ipt = tuple(
space.make_theano_batch(name='monitor_%s' % source,
batch_size=None)
for (space, source) in safe_zip(space_tuple, source_tuple))
# Build a nested tuple from ipt, to dispatch the appropriate parts
# of the ipt batch to each cost
nested_ipt = mapping.nest(ipt)
custom_channels = {}
for i, cost_name in enumerate(cost_names):
if cost_name == '':
prefix = ''
else:
prefix = cost_name + '_'
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
raw_channels = cost.get_monitoring_channels(model, cost_ipt)
channels = {}
for name in raw_channels:
# We need three things: the value itself (raw_channels[name]),
# the input variables (cost_ipt), and the data_specs for
# these input variables ((spaces[i], sources[i]))
channels[prefix + name] = (raw_channels[name], cost_ipt,
(spaces[i], sources[i]))
custom_channels.update(channels)
# Use the last inputs from nested_ipt for the model
model_channels = model.get_monitoring_channels(nested_ipt[-1])
channels = {}
for name in model_channels:
# Note: some code used to consider that model_channels[name]
# could be a a (channel, prereqs) pair, this is not supported.
channels[name] = (model_channels[name], nested_ipt[-1],
(spaces[-1], sources[-1]))
custom_channels.update(channels)
if is_stochastic(mode):
seed = [[2013, 02, 22]]
else:
seed = None
for dataset_name in dataset:
cur_dataset = dataset[dataset_name]
self.add_dataset(dataset=cur_dataset,
mode=mode,
batch_size=batch_size,
num_batches=num_batches,
seed=seed)
if dataset_name == '':
dprefix = ''
else:
dprefix = dataset_name + '_'
# These channel name 'objective' must not vary, since callbacks
# that respond to the values in the monitor use the name to find
# it.
for i, cost_name in enumerate(cost_names):
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
cost_value = cost.expr(model, cost_ipt)
if cost_value is not None:
if cost_name == '':
name = dprefix + 'objective'
prereqs = obj_prereqs
else:
name = dprefix + cost_name
prereqs = None
cost.get_data_specs(model)[0].validate(cost_ipt)
self.add_channel(name=name,
ipt=cost_ipt,
val=cost_value,
data_specs=cost.get_data_specs(model),
dataset=cur_dataset,
prereqs=prereqs)
for key in custom_channels:
val, ipt, data_specs = custom_channels[key]
data_specs[0].validate(ipt)
self.add_channel(name=dprefix + key,
ipt=ipt,
val=val,
data_specs=data_specs,
dataset=cur_dataset)
def setup(self, model, dataset):
"""
Compiles the theano functions needed for the train method.
Parameters
----------
model : a Model instance
dataset : Dataset
"""
if self.cost is None:
self.cost = model.get_default_cost()
inf_params = [param for param in model.get_params()
if np.any(np.isinf(param.get_value()))]
if len(inf_params) > 0:
raise ValueError("These params are Inf: "+str(inf_params))
if any([np.any(np.isnan(param.get_value()))
for param in model.get_params()]):
nan_params = [param for param in model.get_params()
if np.any(np.isnan(param.get_value()))]
raise ValueError("These params are NaN: "+str(nan_params))
self.model = model
self._synchronize_batch_size(model)
model._test_batch_size = self.batch_size
self.monitor = Monitor.get_monitor(model)
self.monitor._sanity_check()
# test if force batch size and batch size
if getattr(model, "force_batch_size", False) and \
any(dataset.get_design_matrix().shape[0] % self.batch_size != 0 for
dataset in self.monitoring_dataset.values()) and \
not has_uniform_batch_size(self.monitor_iteration_mode):
raise ValueError("Dataset size is not a multiple of batch size."
"You should set monitor_iteration_mode to "
"even_sequential, even_shuffled_sequential or "
"even_batchwise_shuffled_sequential")
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
if cost_value is not None and cost_value.name is None:
# Concatenate the name of all tensors in theano_args !?
cost_value.name = 'objective'
# Set up monitor to model the objective value, learning rate,
# momentum (if applicable), and extra channels defined by
# the cost
learning_rate = self.learning_rate
if self.monitoring_dataset is not None:
if (self.monitoring_batch_size is None and
self.monitoring_batches is None):
self.monitoring_batch_size = self.batch_size
self.monitoring_batches = self.batches_per_iter
self.monitor.setup(dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.monitoring_batch_size,
num_batches=self.monitoring_batches,
extra_costs=self.monitoring_costs,
mode=self.monitor_iteration_mode)
dataset_name = self.monitoring_dataset.keys()[0]
monitoring_dataset = self.monitoring_dataset[dataset_name]
#TODO: have Monitor support non-data-dependent channels
self.monitor.add_channel(name='learning_rate',
ipt=None,
val=learning_rate,
data_specs=(NullSpace(), ''),
dataset=monitoring_dataset)
if self.learning_rule:
self.learning_rule.add_channels_to_monitor(
self.monitor,
monitoring_dataset)
params = list(model.get_params())
assert len(params) > 0
for i, param in enumerate(params):
if param.name is None:
param.name = 'sgd_params[%d]' % i
self.params = params
grads, updates = self.cost.get_gradients(model, nested_args,
** fixed_var_descr.fixed_vars)
if not isinstance(grads, OrderedDict):
raise TypeError(str(type(self.cost)) + ".get_gradients returned " +
"something with" + str(type(grads)) + "as its " +
"first member. Expected OrderedDict.")
for param in grads:
assert param in params
for param in params:
assert param in grads
lr_scalers = model.get_lr_scalers()
for key in lr_scalers:
if key not in params:
raise ValueError("Tried to scale the learning rate on " +\
str(key)+" which is not an optimization parameter.")
assert len(updates.keys()) == 0
def get_func(learn_discriminator, learn_generator, dont_you_fucking_dare_touch_the_generator=False):
updates = OrderedDict()
assert (learn_discriminator or learn_generator) and not (learn_discriminator and learn_generator)
if learn_discriminator:
cur_params = model.discriminator.get_params()
else:
cur_params = model.generator.get_params()
def check():
for param in params:
if param not in cur_params:
assert param not in updates
cur_grads = OrderedDict()
for param in cur_params:
cur_grads[param] = grads[param]
for param in grads:
if grads[param].name is None and cost_value is not None:
grads[param].name = ('grad(%(costname)s, %(paramname)s)' %
{'costname': cost_value.name,
'paramname': param.name})
assert grads[param].dtype == param.dtype
cur_lr_scalers = OrderedDict()
for param in cur_params:
if param in lr_scalers:
lr_scaler = lr_scalers[param]
cur_lr_scalers[param] = lr_scaler
log.info('Parameter and initial learning rate summary:')
for param in cur_params:
param_name = param.name
if param_name is None:
param_name = 'anon_param'
lr = learning_rate.get_value() * cur_lr_scalers.get(param,1.)
log.info('\t' + param_name + ': ' + str(lr))
updates.update(self.learning_rule.get_updates(
learning_rate, cur_grads, cur_lr_scalers))
check()
for param in cur_params:
if updates[param].name is None:
updates[param].name = 'sgd_update(' + param.name + ')'
check()
model.modify_updates(updates)
check()
for param in cur_params:
update = updates[param]
if update.name is None:
update.name = 'censor(sgd_update(' + param.name + '))'
for update_val in get_debug_values(update):
if np.any(np.isinf(update_val)):
raise ValueError("debug value of %s contains infs" %
update.name)
if np.any(np.isnan(update_val)):
raise ValueError("debug value of %s contains nans" %
update.name)
check()
if dont_you_fucking_dare_touch_the_generator:
for param in model.generator.get_params():
assert param not in updates
with log_timing(log, 'Compiling sgd_update'):
return function(theano_args,
updates=updates,
name='sgd_update',
on_unused_input='ignore',
mode=self.theano_function_mode)
self.d_func = get_func(1, 0, dont_you_fucking_dare_touch_the_generator=True)
self.g_func = get_func(0, 1)
def setup_training(self):
"""
Sets up training function.
"""
training_batch_size = self.mini_batch_size
cost = self.cnn.get_default_cost()
data_specs = cost.get_data_specs(self.cnn)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=training_batch_size).astype("float32")
theano_args.append(arg)
theano_args = tuple(theano_args)
y_hat = self.cnn.fprop(theano_args[0])
self.fprop_func = theano.function([theano_args[0]], y_hat)
cost = self.cnn.cost(theano_args[1], y_hat)
lr_scalers = self.cnn.get_lr_scalers()
params = list(self.cnn.get_params())
grads = T.grad(cost, params, disconnected_inputs='ignore')
gradients = OrderedDict(izip(params, grads))
rms_vals_dict = OrderedDict(izip(params, self.rms_vals))
updates = OrderedDict()
updates.update(dict(safe_zip(params, [param - self.learning_rate *
(gradients[param] /
T.sqrt(rms_vals_dict[param] + 1e-8))
for param in params])))
rmsprop_updates = OrderedDict()
rmsprop_updates.update(dict(safe_zip(self.rms_vals, [(rms_vals_dict[param] * .9) +
(T.sqr(gradients[param]) * .1)
for param in params])))
self.training = theano.function(theano_args, updates=updates,
on_unused_input='ignore')
self.rmsprop_update = theano.function(theano_args, updates=rmsprop_updates,
on_unused_input='ignore')
temp = T.tensor4()
self.dimshuf_func = theano.function([temp], temp.dimshuffle(1, 2, 3, 0))
#self.grads_func = theano.function(theano_args, grads)
self.cost_function = theano.function(theano_args, cost)
def setup_training(self):
"""
Sets up training function.
"""
training_batch_size = self.mini_batch_size
cost = self.cnn.get_default_cost()
data_specs = cost.get_data_specs(self.cnn)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
#theano_args contains information about the shape of each layer
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=training_batch_size).astype("float32")
theano_args.append(arg)
theano_args = tuple(theano_args)
y_hat = self.cnn.fprop(theano_args[0])
#function used for faster fprop
self.fprop_func = theano.function([theano_args[0]], y_hat)
cost = self.cnn.cost(theano_args[1], y_hat)
#params is the list of layers in the NN
params = list(self.cnn.get_params())
grads = T.grad(cost, params, disconnected_inputs='ignore')
gradients = OrderedDict(izip(params, grads))
rms_vals_dict = OrderedDict(izip(params, self.rms_vals))
momentum_vals_dict = OrderedDict(izip(params, self.momentum_vals))
grad_vals_dict = OrderedDict(izip(params, self.grad_vals))
grad_update = OrderedDict()
grad_update.update(dict(safe_zip(self.grad_vals, [gradients[param]
for param in params])))
#function used for getting gradients
#this is so that we only calculate gradients once, then
#the same values are used for updating momentum, rmsprop, and training
self.grad_update_func = theano.function(theano_args, updates=grad_update,
on_unused_input='ignore')
updates = OrderedDict()
updates.update(dict(safe_zip(params, [param - self.learning_rate *
(grad_vals_dict[param] /
T.sqrt(rms_vals_dict[param] + 1e-8)) +
(self.momentum_step_size *
momentum_vals_dict[param])
for param in params])))
rmsprop_updates = OrderedDict()
#rmsprop update function
rmsprop_updates.update(dict(safe_zip(self.rms_vals, [(rms_vals_dict[param] * .9) +
(T.sqr(grad_vals_dict[param]) * .1)
for param in params])))
self.training = theano.function([], updates=updates,
on_unused_input='ignore')
self.rmsprop_update = theano.function([], updates=rmsprop_updates,
on_unused_input='ignore')
momentum_updates = OrderedDict()
#momentum update function
momentum_updates.update(dict(safe_zip(self.momentum_vals, [-self.learning_rate *
(grad_vals_dict[param] / T.sqrt(rms_vals_dict[param] +
1e-8)) + (self.momentum_step_size *
momentum_vals_dict[param])
for param in params])))
self.momentum_update = theano.function([], updates=momentum_updates,
on_unused_input='ignore')
temp = T.tensor4()
#function used for shuffling dimensions into c01b format
self.dimshuf_func = theano.function([temp], temp.dimshuffle(1, 2, 3, 0))
#functions to get grads and costs for debugging
self.grads_func = theano.function(theano_args, grads)
self.cost_function = theano.function(theano_args, cost)
def setup(self, dataset, cost, batch_size, num_batches=None,
extra_costs=None, mode='sequential', obj_prereqs=None,
cost_monitoring_args=None):
"""
Sets up the monitor for a cost minimization problem.
Adds channels defined by both the model and the cost for
the specified dataset(s), as well as a channel called
'objective' defined by the costs' __call__ method.
Parameters
----------
dataset : pylearn2.datasets.Dataset
Dataset or dictionary mapping string names to Datasets.
If string names are used, then for every dataset, each
channel defined by the model or cost will be replicated
with that dataset's name followed by an underscore as the
prefix. For example, if your cost defines a channel called
'misclass', and datasets is
{'train' : train_dataset, 'valid' : valid_dataset},
you will get channels called 'train_misclass' and
'valid_misclass'.
cost : pylearn2.costs.Cost
The cost being optimized by training. The value of the cost
will appear as the `objective` channel. Its
`get_monitoring_channels` method will also be used to
supply other channels.
extra_costs : OrderedDict, optional
A dictionary mapping channel names to Cost objects.
Their value will appear as the specified channel name.
They will also provide more monitoring channels via their
`get_monitoring_channels` method.
obj_prereqs : None, or list of functions
Functions to pass as prerequisites to the `objective` channel.
cost_monitoring_args : dict
Dictionary of kwargs that will be passed to
`cost.get_monitoring_channels()`
(but not for the extra_costs).
"""
if dataset is None:
return
if isinstance(dataset, Dataset):
dataset = {'': dataset}
else:
assert isinstance(dataset, dict)
assert all(isinstance(key, str) for key in dataset)
assert all(isinstance(dataset[key], Dataset) for key in dataset)
if extra_costs is None:
costs = {}
else:
assert isinstance(extra_costs, (OrderedDict, dict))
costs = extra_costs
assert '' not in costs
costs[''] = cost
if cost_monitoring_args is None:
cost_monitoring_args = {}
model = self.model
# Build a composite data_specs containing the specs for all costs,
# then the specs of the model
cost_names = sorted(costs.keys())
spaces = []
sources = []
for c in cost_names:
c_space, c_source = costs[c].get_data_specs(model)
spaces.append(c_space)
sources.append(c_source)
# Ask the model for the data_specs needed
m_space, m_source = model.get_monitoring_data_specs()
spaces.append(m_space)
sources.append(m_source)
nested_space = CompositeSpace(spaces)
nested_sources = tuple(sources)
# Flatten this data_specs, so we build only one symbolic Theano
# variable for each of the unique (space, source) pairs.
mapping = DataSpecsMapping((nested_space, nested_sources))
space_tuple = mapping.flatten(nested_space, return_tuple=True)
source_tuple = mapping.flatten(nested_sources, return_tuple=True)
ipt = tuple(space.make_theano_batch(name='monitor_%s' % source,
batch_size=None)
for (space, source) in safe_zip(space_tuple, source_tuple))
# Build a nested tuple from ipt, to dispatch the appropriate parts
# of the ipt batch to each cost
nested_ipt = mapping.nest(ipt)
custom_channels = {}
for i, cost_name in enumerate(cost_names):
if cost_name == '':
prefix = ''
else:
prefix = cost_name + '_'
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
raw_channels = cost.get_monitoring_channels(model, cost_ipt)
channels = {}
for name in raw_channels:
# We need three things: the value itself (raw_channels[name]),
# the input variables (cost_ipt), and the data_specs for
# these input variables ((spaces[i], sources[i]))
channels[prefix + name] = (raw_channels[name],
cost_ipt,
(spaces[i], sources[i]))
custom_channels.update(channels)
# Use the last inputs from nested_ipt for the model
model_channels = model.get_monitoring_channels(nested_ipt[-1])
channels = {}
for name in model_channels:
# Note: some code used to consider that model_channels[name]
# could be a a (channel, prereqs) pair, this is not supported.
channels[name] = (model_channels[name],
nested_ipt[-1],
(spaces[-1], sources[-1]))
custom_channels.update(channels)
if is_stochastic(mode):
seed = [[2013, 02, 22]]
else:
seed = None
for dataset_name in dataset:
cur_dataset = dataset[dataset_name]
self.add_dataset(dataset=cur_dataset,
mode=mode,
batch_size=batch_size,
num_batches=num_batches,
seed=seed)
if dataset_name == '':
dprefix = ''
else:
dprefix = dataset_name + '_'
# These channel name 'objective' must not vary, since callbacks
# that respond to the values in the monitor use the name to find
# it.
for i, cost_name in enumerate(cost_names):
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
cost_value = cost.expr(model, cost_ipt)
if cost_value is not None:
if cost_name == '':
name = dprefix + 'objective'
prereqs = obj_prereqs
else:
name = dprefix + cost_name
prereqs = None
cost.get_data_specs(model)[0].validate(cost_ipt)
self.add_channel(name=name,
ipt=cost_ipt,
val=cost_value,
data_specs=cost.get_data_specs(model),
dataset=cur_dataset,
prereqs=prereqs)
for key in custom_channels:
val, ipt, data_specs = custom_channels[key]
data_specs[0].validate(ipt)
self.add_channel(name=dprefix + key,
ipt=ipt,
val=val,
data_specs=data_specs,
dataset=cur_dataset)
def __init__(self, data_callbacks, data_specs):
self.data_callbacks = data_callbacks
self.data_specs = data_specs
self._mapping = DataSpecsMapping(data_specs)
def add_channel(self,
name,
ipt,
val,
dataset=None,
prereqs=None,
data_specs=None):
"""
Asks the monitor to start tracking a new value. Can be called even
after the monitor is already in use.
Parameters
----------
name : str
The display name in the monitor.
ipt : tensor_like
The symbolic tensor which should be clamped to the data. \
(or a list/tuple containing symbolic tensors, following the \
data_specs)
val : tensor_like
The value (function of `ipt`) to be tracked.
dataset : pylearn2.datasets.Dataset
Which dataset to compute this channel on
prereqs : list of callables that take a list of numpy tensors
Each prereq must be called exactly once per each new batch of \
data drawn *from dataset* before the channel value is computed \
if two channels provide a prereq with exactly the same id, that \
prereq will only be called once
data_specs : (space, source) pair
Identifies the order, format and semantics of ipt
"""
if isinstance(val, (float, int, long)):
val = np.cast[theano.config.floatX](val)
val = T.as_tensor_variable(val)
if data_specs is None:
warnings.warn("parameter 'data_specs' should be provided when " +
"calling add_channel. We will build a default one.",
stacklevel=2)
if isinstance(ipt, list):
ipt = tuple(ipt)
if ipt is not None and not isinstance(ipt, tuple):
ipt = (ipt, )
if ipt is None:
data_specs = (NullSpace(), '')
elif len(ipt) == 0:
data_specs = (CompositeSpace([]), ())
elif hasattr(dataset, 'get_data_specs'):
dataset_space, dataset_source = dataset.get_data_specs()
if (len(ipt) == 1 and dataset_source is not None
and (not isinstance(dataset_source, tuple)
or len(dataset_source) == 1)
and 'features' in dataset_source):
data_specs = (dataset_space, dataset_source)
elif (len(ipt) == 2
and dataset_source == ('features', 'targets')):
data_specs = (dataset_space, dataset_source)
else:
raise ValueError("Cannot infer default data_specs for " +
"the following input points and " +
"dataset: ipt = %s, dataset = %s" %
(ipt, dataset))
data_specs[0].validate(ipt)
mapping = DataSpecsMapping(data_specs)
flat_ipt = mapping.flatten(ipt)
if not isinstance(flat_ipt, tuple):
flat_ipt = (flat_ipt, )
inputs = theano.gof.graph.inputs([val])
for elem in inputs:
if not hasattr(elem, 'get_value') and \
not isinstance(elem, theano.gof.graph.Constant):
if elem not in flat_ipt:
raise ValueError("Unspecified input: " + str(elem) +
". This may be due to an incorrect " +
"implementation of a cost's " +
"get_data_specs() method, or of a " +
"model's get_monitoring_data_specs() " +
"method.")
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('Adding monitor channel ' + name + '\n')
assert isinstance(flat_ipt, tuple)
if len(flat_ipt) != 1:
for elem in flat_ipt:
mode.record.handle_line('Includes input var ' +
var_descriptor(elem) + '\n')
else:
mode.record.handle_line(name + ' input var is ' +
var_descriptor(flat_ipt[0]) + '\n')
mode.record.handle_line('channel ' + name + ' is ' +
var_descriptor(val) + '\n')
if dataset is None:
if len(self._datasets) == 1:
dataset = self._datasets[0]
elif len(self._datasets) == 0:
raise ValueError(_err_no_data)
else:
raise ValueError(_err_ambig_data)
try:
self._datasets.index(dataset)
except ValueError:
raise ValueError("The dataset specified is not one of the " +
"monitor's datasets")
if name in self.channels:
raise ValueError("Tried to create the same channel twice (%s)" %
name)
self.channels[name] = MonitorChannel(ipt, val, name, data_specs,
dataset, prereqs)
self._dirty = True
def __init__(self,
nmap,
input_space=None,
nvisx=None,
nvisy=None,
input_source=("featuresX", "featuresY"),
act_enc=None,
act_dec=None,
irange=1e-3,
rng=9001):
Block.__init__(self)
Model.__init__(self)
assert nmap > 0, "Number of mapping units must be positive"
if nvisx is not None and nvisy is not None or input_space is not None:
if nvisx is not None and nvisy is not None:
assert nvisx > 0, "Number of visx units must be non-negative"
assert nvisy > 0, "Number of visy units must be non-negative"
input_space = CompositeSpace(
[VectorSpace(nvisx),
VectorSpace(nvisy)])
self.nvisx = nvisx
self.nvisy = nvisy
elif isinstance(input_space.components[0], Conv2DSpace):
rx, cx = input_space.components[0].shape
chx = input_space.components[0].num_channels
ry, cy = input_space.components[1].shape
chy = input_space.components[1].num_channels
self.nvisx = rx * cx * chx
self.nvisy = ry * cy * chy
else:
raise NotImplementedError(
str(type(self)) + " does not support that input_space.")
# Check whether the input_space and input_source structures match
try:
DataSpecsMapping((input_space, input_source))
except ValueError:
raise ValueError("The structures of `input_space`, %s, and "
"`input_source`, %s do not match. If you "
"specified a CompositeSpace as an input, "
"be sure to specify the data sources as well." %
(input_space, input_source))
self.input_space = input_space
self.input_source = input_source
self.nmap = nmap
self.output_space = VectorSpace(self.nmap)
self._initialize_visbiasX(self.nvisx) # self.visbiasX
self._initialize_visbiasY(self.nvisy) # self.visbiasY
self._initialize_mapbias() # self.mapbias
self.irange = irange
self.rng = make_np_rng(rng, which_method="randn")
seed = int(self.rng.randint(2**30))
self.s_rng = make_theano_rng(seed, which_method="uniform")
def _resolve_callable(conf, conf_attr):
if conf[conf_attr] is None or conf[conf_attr] == "linear":
return None
# If it's a callable, use it directly.
if hasattr(conf[conf_attr], '__call__'):
return conf[conf_attr]
elif (conf[conf_attr] in globals()
and hasattr(globals()[conf[conf_attr]], '__call__')):
return globals()[conf[conf_attr]]
elif hasattr(tensor.nnet, conf[conf_attr]):
return getattr(tensor.nnet, conf[conf_attr])
elif hasattr(tensor, conf[conf_attr]):
return getattr(tensor, conf[conf_attr])
else:
raise ValueError("Couldn't interpret %s value: '%s'" %
(conf_attr, conf[conf_attr]))
self.act_enc = _resolve_callable(locals(), 'act_enc')
self.act_dec = _resolve_callable(locals(), 'act_dec')
def redo_theano(self):
"""
Recompiles Theano functions used by this monitor.
This is called any time we need to evaluate the channels and
the channel definitions have changed since last we called it,
or if the theano functions are unavailable for any other reason
(first time they are needed after construction or
deserialization, etc.)
All channels are compiled as part of the same theano function
so that the theano optimizations can eliminate subexpressions
that are shared between multiple channels.
"""
self._dirty = False
# Recompute the data specs, since the channels may have changed.
self._build_data_specs()
init_names = dir(self)
self.prereqs = OrderedDict()
for channel in self.channels.values():
if channel.prereqs is not None:
dataset = channel.dataset
if dataset not in self.prereqs:
self.prereqs[dataset] = []
prereqs = self.prereqs[dataset]
for prereq in channel.prereqs:
if prereq not in prereqs:
prereqs.append(prereq)
updates = OrderedDict()
for channel in self.channels.values():
updates[channel.val_shared] = np.cast[config.floatX](0.0)
with log_timing(log, "compiling begin_record_entry"):
self.begin_record_entry = function(
inputs=[],
updates=updates,
mode=self.theano_function_mode,
name='Monitor.begin_record_entry'
)
updates = OrderedDict()
givens = OrderedDict()
# Get the appropriate kind of theano variable to represent the data
# the model acts on
batch_names = ['monitoring_%s' % s for s in self._flat_data_specs[1]]
theano_args = self._flat_data_specs[0].make_theano_batch(batch_names)
# Get a symbolic expression of the batch size
# We do it here, rather than for each channel, because channels with an
# empty data_specs do not use data, and are unable to extract the batch
# size. The case where the whole data specs is empty is not supported.
batch_size = self._flat_data_specs[0].batch_size(theano_args)
# Also get a nested representation, for joint iteration
# with each of channel.graph_input
nested_theano_args = self._data_specs_mapping.nest(theano_args)
if not isinstance(nested_theano_args, tuple):
nested_theano_args = (nested_theano_args,)
assert len(nested_theano_args) == (len(self.channels) + 1)
log.info('Monitored channels: ')
for key in sorted(self.channels.keys()):
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling monitor including ' +
'channel ' + key + '\n')
log.info('\t%s' % key)
it = [d.iterator(mode=i, num_batches=n, batch_size=b,
data_specs=self._flat_data_specs,
return_tuple=True)
for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
self._num_batches, self._batch_size)]
self.num_examples = [np.cast[config.floatX](float(i.num_examples))
for i in it]
self.num_examples = [float(i.num_examples) for i in it]
givens = [OrderedDict() for d in self._datasets]
updates = [OrderedDict() for d in self._datasets]
for i, channel in enumerate(self.channels.values()):
index = self._datasets.index(channel.dataset)
d = self._datasets[index]
g = givens[index]
inv_cur_num_examples = as_floatX(1./self.num_examples[index])
u = updates[index]
# Flatten channel.graph_input and the appropriate part of
# nested_theano_args, to iterate jointly over them.
c_mapping = DataSpecsMapping(channel.data_specs)
channel_inputs = c_mapping.flatten(channel.graph_input,
return_tuple=True)
inputs = c_mapping.flatten(nested_theano_args[i + 1],
return_tuple=True)
for (channel_X, X) in safe_izip(channel_inputs, inputs):
assert channel_X not in g or g[channel_X] is X
assert channel_X.type == X.type, (channel_X.type, X.type)
g[channel_X] = X
if batch_size == 0:
# No channel does need any data, so there is not need to
# average results, and we will call the accum functions only
# once.
# TODO: better handling of channels not needing data when
# some other channels need data.
assert len(self._flat_data_specs[1]) == 0
val = channel.val
else:
if n == 0:
raise ValueError("Iterating over 0 examples results in " +
"divide by 0")
val = (channel.val * T.cast(batch_size, config.floatX)
* inv_cur_num_examples)
u[channel.val_shared] = channel.val_shared + val
with log_timing(log, "Compiling accum"):
# Check type of update expressions
for up in updates:
for key in up:
if key.dtype != up[key].dtype:
raise TypeError('Monitoring channel shared variable ' +
key.name + ' has dtype ' + key.dtype +
' but is driven by an expression ' +
'with type ' + up[key].dtype)
self.accum = []
for idx, packed in enumerate(safe_izip(givens, updates)):
g, u = packed
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
for elem in g:
mode.record.handle_line('g key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('g val ' +
var_descriptor(g[elem]) + '\n')
for elem in u:
mode.record.handle_line('u key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('u val ' +
var_descriptor(u[elem]) + '\n')
function_name = 'Monitor.accum[%d]' % idx
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling supervised accum\n')
# Some channels may not depend on the data, ie, they might just
# monitor the model parameters, or some shared variable updated
# by the training algorithm, so we need to ignore the unused
# input error
self.accum.append(function(theano_args,
givens=g,
updates=u,
mode=self.theano_function_mode,
name=function_name))
for a in self.accum:
if mode is not None and hasattr(mode, 'record'):
for elem in a.maker.fgraph.outputs:
mode.record.handle_line('accum output ' +
var_descriptor(elem) + '\n')
log.info("graph size: %d" % len(a.maker.fgraph.toposort()))
final_names = dir(self)
self.register_names_to_del([name for name in final_names
if name not in init_names])
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 : object
A Python object representing the model to train loosely \
implementing the interface of models.model.Model.
dataset : pylearn2.datasets.dataset.Dataset
Dataset object used to draw training data
"""
self.model = model
if self.cost is None:
self.cost = model.get_default_cost()
if self.batch_size is None:
self.batch_size = model.force_batch_size
else:
batch_size = self.batch_size
if self.set_batch_size:
model.set_batch_size(batch_size)
elif hasattr(model, 'force_batch_size'):
if not (model.force_batch_size <= 0 or batch_size ==
model.force_batch_size):
raise ValueError("batch_size is %d but " +
"model.force_batch_size is %d" %
(batch_size, model.force_batch_size))
self.monitor = Monitor.get_monitor(model)
self.monitor.set_theano_function_mode(self.theano_function_mode)
data_specs = self.cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space,
# named according to the sources.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = 'BGD_[%s]' % source
arg = space.make_theano_batch(name=name)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with their data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
grads, grad_updates = self.cost.get_gradients(
model, nested_args, ** fixed_var_descr.fixed_vars)
assert isinstance(grads, OrderedDict)
assert isinstance(grad_updates, OrderedDict)
if cost_value is None:
raise ValueError("BGD is incompatible with " + str(self.cost) +
" because it is intractable, but BGD uses the " +
"cost function value to do line searches.")
# obj_prereqs has to be a list of function f called with f(*data),
# where data is a data tuple coming from the iterator.
# this function enables capturing "mapping" and "f", while
# enabling the "*data" syntax
def capture(f, mapping=mapping):
new_f = lambda *args: f(mapping.flatten(args, return_tuple=True))
return new_f
obj_prereqs = [capture(f) for f in fixed_var_descr.on_load_batch]
if self.monitoring_dataset is not None:
self.monitor.setup(
dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.batch_size,
num_batches=self.monitoring_batches,
obj_prereqs=obj_prereqs,
cost_monitoring_args=fixed_var_descr.fixed_vars)
# TODO : Why is this commented?
'''
channels = model.get_monitoring_channels(theano_args)
if not isinstance(channels, dict):
raise TypeError("model.get_monitoring_channels must return a "
"dictionary, but it returned " + str(channels))
channels.update(self.cost.get_monitoring_channels(model, theano_args, ** fixed_var_descr.fixed_vars))
for dataset_name in self.monitoring_dataset:
if dataset_name == '':
prefix = ''
else:
prefix = dataset_name + '_'
monitoring_dataset = self.monitoring_dataset[dataset_name]
self.monitor.add_dataset(dataset=monitoring_dataset,
mode="sequential",
batch_size=self.batch_size,
num_batches=self.monitoring_batches)
# The monitor compiles all channels for the same dataset into one function, and
# runs all prereqs before calling the function. So we only need to register the
# on_load_batch prereq once per monitoring dataset.
self.monitor.add_channel(prefix + 'objective',ipt=ipt,val=cost_value,
dataset = monitoring_dataset, prereqs = fixed_var_descr.on_load_batch)
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= prefix + name,
ipt=ipt,
val=J,
data_specs=data_specs,
dataset = monitoring_dataset,
prereqs=prereqs)
'''
params = model.get_params()
self.optimizer = BatchGradientDescent(
objective = cost_value,
gradients = grads,
gradient_updates = grad_updates,
params = params,
param_constrainers = [ model.censor_updates ],
lr_scalers = model.get_lr_scalers(),
inputs = theano_args,
verbose = self.verbose_optimization,
max_iter = self.updates_per_batch,
reset_alpha = self.reset_alpha,
conjugate = self.conjugate,
reset_conjugate = self.reset_conjugate,
min_init_alpha = self.min_init_alpha,
line_search_mode = self.line_search_mode,
theano_function_mode=self.theano_function_mode,
init_alpha=self.init_alpha)
# These monitoring channels keep track of shared variables,
# which do not need inputs nor data.
if self.monitoring_dataset is not None:
self.monitor.add_channel(
name='ave_step_size',
ipt=None,
val=self.optimizer.ave_step_size,
data_specs=(NullSpace(), ''),
dataset=self.monitoring_dataset.values()[0])
self.monitor.add_channel(
name='ave_grad_size',
ipt=None,
val=self.optimizer.ave_grad_size,
data_specs=(NullSpace(), ''),
dataset=self.monitoring_dataset.values()[0])
self.monitor.add_channel(
name='ave_grad_mult',
ipt=None,
val=self.optimizer.ave_grad_mult,
data_specs=(NullSpace(), ''),
dataset=self.monitoring_dataset.values()[0])
self.first = True
self.bSetup = True
def setup(self, model, dataset):
"""
Compiles the theano functions needed for the train method.
"""
if self.cost is None:
self.cost = model.get_default_cost()
inf_params = [param for param in model.get_params()
if np.any(np.isinf(param.get_value()))]
if len(inf_params) > 0:
raise ValueError("These params are Inf: "+str(inf_params))
if any([np.any(np.isnan(param.get_value()))
for param in model.get_params()]):
nan_params = [param for param in model.get_params()
if np.any(np.isnan(param.get_value()))]
raise ValueError("These params are NaN: "+str(nan_params))
self.model = model
self._synchronize_batch_size(model)
model._test_batch_size = self.batch_size
self.monitor = Monitor.get_monitor(model)
self.monitor._sanity_check()
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
if cost_value is not None and cost_value.name is None:
# Concatenate the name of all tensors in theano_args !?
cost_value.name = 'objective'
# Set up monitor to model the objective value, learning rate,
# momentum (if applicable), and extra channels defined by
# the cost
learning_rate = self.learning_rate
if self.monitoring_dataset is not None:
self.monitor.setup(dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.batch_size,
num_batches=self.monitoring_batches,
extra_costs=self.monitoring_costs,
mode=self.monitor_iteration_mode)
dataset_name = self.monitoring_dataset.keys()[0]
monitoring_dataset = self.monitoring_dataset[dataset_name]
#TODO: have Monitor support non-data-dependent channels
self.monitor.add_channel(name='learning_rate',
ipt=None,
val=learning_rate,
data_specs=(NullSpace(), ''),
dataset=monitoring_dataset)
if self.learning_rule:
self.learning_rule.add_channels_to_monitor(
self.monitor,
monitoring_dataset)
params = list(model.get_params())
assert len(params) > 0
for i, param in enumerate(params):
if param.name is None:
param.name = 'sgd_params[%d]' % i
grads, updates = self.cost.get_gradients(model, nested_args,
** fixed_var_descr.fixed_vars)
if not isinstance(grads, OrderedDict):
raise TypeError(str(type(self.cost)) + ".get_gradients returned " +
"something with" + str(type(grads)) + "as its " +
"first member. Expected OrderedDict.")
for param in grads:
assert param in params
for param in params:
assert param in grads
for param in grads:
if grads[param].name is None and cost_value is not None:
grads[param].name = ('grad(%(costname)s, %(paramname)s)' %
{'costname': cost_value.name,
'paramname': param.name})
assert grads[param].dtype == param.dtype
lr_scalers = model.get_lr_scalers()
for key in lr_scalers:
if key not in params:
raise ValueError("Tried to scale the learning rate on " +\
str(key)+" which is not an optimization parameter.")
log.info('Parameter and initial learning rate summary:')
for param in params:
param_name = param.name
if param_name is None:
param_name = 'anon_param'
lr = learning_rate.get_value() * lr_scalers.get(param,1.)
log.info('\t' + param_name + ': ' + str(lr))
if self.learning_rule:
updates.update(self.learning_rule.get_updates(
learning_rate, grads, lr_scalers))
else:
# Use standard SGD updates with fixed learning rate.
updates.update( dict(safe_zip(params, [param - learning_rate * \
lr_scalers.get(param, 1.) * grads[param]
for param in params])))
for param in params:
if updates[param].name is None:
updates[param].name = 'sgd_update(' + param.name + ')'
model.censor_updates(updates)
for param in params:
update = updates[param]
if update.name is None:
update.name = 'censor(sgd_update(' + param.name + '))'
for update_val in get_debug_values(update):
if np.any(np.isinf(update_val)):
raise ValueError("debug value of %s contains infs" %
update.name)
if np.any(np.isnan(update_val)):
raise ValueError("debug value of %s contains nans" %
update.name)
with log_timing(log, 'Compiling sgd_update'):
self.sgd_update = function(theano_args,
updates=updates,
name='sgd_update',
on_unused_input='ignore',
mode=self.theano_function_mode)
self.params = params
def redo_theano(self):
"""
Recompiles Theano functions used by this monitor.
This is called any time we need to evaluate the channels and the
channel definitions have changed since last we called it, or if the
theano functions are unavailable for any other reason (first time they
are needed after construction or deserialization, etc.)
All channels are compiled as part of the same theano function so that
the theano optimizations can eliminate subexpressions that are shared
between multiple channels.
"""
self._dirty = False
# Recompute the data specs, since the channels may have changed.
self._build_data_specs()
init_names = dir(self)
self.prereqs = OrderedDict()
for channel in self.channels.values():
if channel.prereqs is not None:
dataset = channel.dataset
if dataset not in self.prereqs:
self.prereqs[dataset] = []
prereqs = self.prereqs[dataset]
for prereq in channel.prereqs:
if prereq not in prereqs:
prereqs.append(prereq)
updates = OrderedDict()
for channel in self.channels.values():
updates[channel.val_shared] = np.cast[config.floatX](0.0)
with log_timing(log, "compiling begin_record_entry"):
self.begin_record_entry = function(
inputs=[],
updates=updates,
mode=self.theano_function_mode,
name='Monitor.begin_record_entry')
updates = OrderedDict()
givens = OrderedDict()
# Get the appropriate kind of theano variable to represent the data
# the model acts on
batch_names = ['monitoring_%s' % s for s in self._flat_data_specs[1]]
theano_args = self._flat_data_specs[0].make_theano_batch(batch_names)
# Get a symbolic expression of the batch size
# We do it here, rather than for each channel, because channels with an
# empty data_specs do not use data, and are unable to extract the batch
# size. The case where the whole data specs is empty is not supported.
batch_size = self._flat_data_specs[0].batch_size(theano_args)
# Also get a nested representation, for joint iteration
# with each of channel.graph_input
nested_theano_args = self._data_specs_mapping.nest(theano_args)
if not isinstance(nested_theano_args, tuple):
nested_theano_args = (nested_theano_args, )
assert len(nested_theano_args) == (len(self.channels) + 1)
log.info('Monitored channels: ')
for key in sorted(self.channels.keys()):
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling monitor including ' +
'channel ' + key + '\n')
log.info('\t%s' % key)
it = [
d.iterator(mode=i,
num_batches=n,
batch_size=b,
data_specs=self._flat_data_specs,
return_tuple=True)
for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
self._num_batches, self._batch_size)
]
self.num_examples = [
np.cast[config.floatX](float(i.num_examples)) for i in it
]
givens = [OrderedDict() for d in self._datasets]
updates = [OrderedDict() for d in self._datasets]
for i, channel in enumerate(self.channels.values()):
index = self._datasets.index(channel.dataset)
d = self._datasets[index]
g = givens[index]
cur_num_examples = self.num_examples[index]
u = updates[index]
# Flatten channel.graph_input and the appropriate part of
# nested_theano_args, to iterate jointly over them.
c_mapping = DataSpecsMapping(channel.data_specs)
channel_inputs = c_mapping.flatten(channel.graph_input,
return_tuple=True)
inputs = c_mapping.flatten(nested_theano_args[i + 1],
return_tuple=True)
for (channel_X, X) in safe_izip(channel_inputs, inputs):
assert channel_X not in g or g[channel_X] is X
assert channel_X.type == X.type, (channel_X.type, X.type)
g[channel_X] = X
if batch_size == 0:
# No channel does need any data, so there is not need to
# average results, and we will call the accum functions only
# once.
# TODO: better handling of channels not needing data when
# some other channels need data.
assert len(self._flat_data_specs[1]) == 0
val = channel.val
else:
if n == 0:
raise ValueError("Iterating over 0 examples results in " +
"divide by 0")
val = (channel.val * T.cast(batch_size, config.floatX) /
cur_num_examples)
u[channel.val_shared] = channel.val_shared + val
with log_timing(log, "Compiling accum"):
# Check type of update expressions
for up in updates:
for key in up:
if key.dtype != up[key].dtype:
raise TypeError('Monitoring channel shared variable ' +
key.name + ' has dtype ' + key.dtype +
' but is driven by an expression ' +
'with type ' + up[key].dtype)
self.accum = []
for idx, packed in enumerate(safe_izip(givens, updates)):
g, u = packed
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
for elem in g:
mode.record.handle_line('g key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('g val ' +
var_descriptor(g[elem]) + '\n')
for elem in u:
mode.record.handle_line('u key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('u val ' +
var_descriptor(u[elem]) + '\n')
function_name = 'Monitor.accum[%d]' % idx
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling supervised accum\n')
# Some channels may not depend on the data, ie, they might just
# monitor the model parameters, or some shared variable updated
# by the training algorithm, so we need to ignore the unused
# input error
self.accum.append(
function(theano_args,
givens=g,
updates=u,
mode=self.theano_function_mode,
name=function_name))
for a in self.accum:
if mode is not None and hasattr(mode, 'record'):
for elem in a.maker.fgraph.outputs:
mode.record.handle_line('accum output ' +
var_descriptor(elem) + '\n')
log.info("graph size: %d" % len(a.maker.fgraph.toposort()))
final_names = dir(self)
self.register_names_to_del(
[name for name in final_names if name not in init_names])
def setup(self, model, dataset):
"""
Compiles the theano functions needed for the train method.
"""
if self.cost is None:
self.cost = model.get_default_cost()
inf_params = [param for param in model.get_params()
if np.any(np.isinf(param.get_value()))]
if len(inf_params) > 0:
raise ValueError("These params are Inf: "+str(inf_params))
if any([np.any(np.isnan(param.get_value()))
for param in model.get_params()]):
nan_params = [param for param in model.get_params()
if np.any(np.isnan(param.get_value()))]
raise ValueError("These params are NaN: "+str(nan_params))
self.model = model
batch_size = self.batch_size
if hasattr(model, "force_batch_size"):
if model.force_batch_size > 0:
if batch_size is not None:
if batch_size != model.force_batch_size:
if self.set_batch_size:
model.set_batch_size(batch_size)
else:
raise ValueError("batch_size argument to SGD " +
"conflicts with model's " +
"force_batch_size attribute")
else:
self.batch_size = model.force_batch_size
model._test_batch_size = self.batch_size
self.monitor = Monitor.get_monitor(model)
self.monitor._sanity_check()
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
if cost_value is not None and cost_value.name is None:
# Concatenate the name of all tensors in theano_args !?
cost_value.name = 'objective'
# Set up monitor to model the objective value, learning rate,
# momentum (if applicable), and extra channels defined by
# the cost
learning_rate = self.learning_rate
if self.monitoring_dataset is not None:
self.monitor.setup(
dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.batch_size,
num_batches=self.monitoring_batches,
extra_costs=self.monitoring_costs,
mode=self.monitor_iteration_mode
)
dataset_name = self.monitoring_dataset.keys()[0]
monitoring_dataset = self.monitoring_dataset[dataset_name]
#TODO: have Monitor support non-data-dependent channels
self.monitor.add_channel(name='learning_rate',
ipt=None,
val=learning_rate,
data_specs=(NullSpace(), ''),
dataset=monitoring_dataset)
if self.learning_rule:
self.learning_rule.add_channels_to_monitor(
self.monitor,
monitoring_dataset)
params = list(model.get_params())
assert len(params) > 0
for i, param in enumerate(params):
if param.name is None:
param.name = 'sgd_params[%d]' % i
grads, updates = self.cost.get_gradients(model, nested_args,
** fixed_var_descr.fixed_vars)
if not isinstance(grads, OrderedDict):
raise TypeError(str(type(self.cost)) + ".get_gradients returned " +
"something with" + str(type(grads)) + "as its " +
"first member. Expected OrderedDict.")
for param in grads:
assert param in params
for param in params:
assert param in grads
for param in grads:
if grads[param].name is None and cost_value is not None:
grads[param].name = ('grad(%(costname)s, %(paramname)s)' %
{'costname': cost_value.name,
'paramname': param.name})
assert grads[param].dtype == param.dtype
lr_scalers = model.get_lr_scalers()
for key in lr_scalers:
if key not in params:
raise ValueError("Tried to scale the learning rate on " +\
str(key)+" which is not an optimization parameter.")
log.info('Parameter and initial learning rate summary:')
for param in params:
param_name = param.name
if param_name is None:
param_name = 'anon_param'
lr = learning_rate.get_value() * lr_scalers.get(param,1.)
log.info('\t' + param_name + ': ' + str(lr))
if self.learning_rule:
updates.update(self.learning_rule.get_updates(
learning_rate, grads, lr_scalers))
else:
# Use standard SGD updates with fixed learning rate.
updates.update( dict(safe_zip(params, [param - learning_rate * \
lr_scalers.get(param, 1.) * grads[param]
for param in params])))
for param in params:
if updates[param].name is None:
updates[param].name = 'sgd_update(' + param.name + ')'
model.censor_updates(updates)
for param in params:
update = updates[param]
if update.name is None:
update.name = 'censor(sgd_update(' + param.name + '))'
for update_val in get_debug_values(update):
if np.any(np.isinf(update_val)):
raise ValueError("debug value of %s contains infs" % update.name)
if np.any(np.isnan(update_val)):
raise ValueError("debug value of %s contains nans" % update.name)
with log_timing(log, 'Compiling sgd_update'):
self.sgd_update = function(theano_args,
updates=updates,
name='sgd_update',
on_unused_input='ignore',
mode=self.theano_function_mode)
self.params = params
def setup(self, model, dataset):
"""
Compiles the theano functions needed for the train method.
Parameters
----------
model : a Model instance
dataset : Dataset
"""
if self.cost is None:
self.cost = model.get_default_cost()
inf_params = [
param for param in model.get_params()
if np.any(np.isinf(param.get_value()))
]
if len(inf_params) > 0:
raise ValueError("These params are Inf: " + str(inf_params))
if any([
np.any(np.isnan(param.get_value()))
for param in model.get_params()
]):
nan_params = [
param for param in model.get_params()
if np.any(np.isnan(param.get_value()))
]
raise ValueError("These params are NaN: " + str(nan_params))
self.model = model
self._synchronize_batch_size(model)
model._test_batch_size = self.batch_size
self.monitor = Monitor.get_monitor(model)
self.monitor._sanity_check()
# test if force batch size and batch size
if getattr(model, "force_batch_size", False) and \
any(dataset.get_design_matrix().shape[0] % self.batch_size != 0 for
dataset in self.monitoring_dataset.values()) and \
not has_uniform_batch_size(self.monitor_iteration_mode):
raise ValueError("Dataset size is not a multiple of batch size."
"You should set monitor_iteration_mode to "
"even_sequential, even_shuffled_sequential or "
"even_batchwise_shuffled_sequential")
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args,
**fixed_var_descr.fixed_vars)
if cost_value is not None and cost_value.name is None:
# Concatenate the name of all tensors in theano_args !?
cost_value.name = 'objective'
# Set up monitor to model the objective value, learning rate,
# momentum (if applicable), and extra channels defined by
# the cost
learning_rate = self.learning_rate
if self.monitoring_dataset is not None:
if (self.monitoring_batch_size is None
and self.monitoring_batches is None):
self.monitoring_batch_size = self.batch_size
self.monitoring_batches = self.batches_per_iter
self.monitor.setup(dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.monitoring_batch_size,
num_batches=self.monitoring_batches,
extra_costs=self.monitoring_costs,
mode=self.monitor_iteration_mode)
dataset_name = self.monitoring_dataset.keys()[0]
monitoring_dataset = self.monitoring_dataset[dataset_name]
#TODO: have Monitor support non-data-dependent channels
self.monitor.add_channel(name='learning_rate',
ipt=None,
val=learning_rate,
data_specs=(NullSpace(), ''),
dataset=monitoring_dataset)
if self.learning_rule:
self.learning_rule.add_channels_to_monitor(
self.monitor, monitoring_dataset)
params = list(model.get_params())
assert len(params) > 0
for i, param in enumerate(params):
if param.name is None:
param.name = 'sgd_params[%d]' % i
self.params = params
grads, updates = self.cost.get_gradients(model, nested_args,
**fixed_var_descr.fixed_vars)
if not isinstance(grads, OrderedDict):
raise TypeError(
str(type(self.cost)) + ".get_gradients returned " +
"something with" + str(type(grads)) + "as its " +
"first member. Expected OrderedDict.")
for param in grads:
assert param in params
for param in params:
assert param in grads
lr_scalers = model.get_lr_scalers()
for key in lr_scalers:
if key not in params:
raise ValueError("Tried to scale the learning rate on " +\
str(key)+" which is not an optimization parameter.")
assert len(updates.keys()) == 0
def get_func(learn_discriminator,
learn_generator,
dont_you_fucking_dare_touch_the_generator=False):
updates = OrderedDict()
assert (learn_discriminator or learn_generator
) and not (learn_discriminator and learn_generator)
if learn_discriminator:
cur_params = model.discriminator.get_params()
else:
cur_params = model.generator.get_params()
def check():
for param in params:
if param not in cur_params:
assert param not in updates
cur_grads = OrderedDict()
for param in cur_params:
cur_grads[param] = grads[param]
for param in grads:
if grads[param].name is None and cost_value is not None:
grads[param].name = ('grad(%(costname)s, %(paramname)s)' %
{
'costname': cost_value.name,
'paramname': param.name
})
assert grads[param].dtype == param.dtype
cur_lr_scalers = OrderedDict()
for param in cur_params:
if param in lr_scalers:
lr_scaler = lr_scalers[param]
cur_lr_scalers[param] = lr_scaler
log.info('Parameter and initial learning rate summary:')
for param in cur_params:
param_name = param.name
if param_name is None:
param_name = 'anon_param'
lr = learning_rate.get_value() * cur_lr_scalers.get(param, 1.)
log.info('\t' + param_name + ': ' + str(lr))
updates.update(
self.learning_rule.get_updates(learning_rate, cur_grads,
cur_lr_scalers))
check()
for param in cur_params:
if updates[param].name is None:
updates[param].name = 'sgd_update(' + param.name + ')'
check()
model.modify_updates(updates)
check()
for param in cur_params:
update = updates[param]
if update.name is None:
update.name = 'censor(sgd_update(' + param.name + '))'
for update_val in get_debug_values(update):
if np.any(np.isinf(update_val)):
raise ValueError("debug value of %s contains infs" %
update.name)
if np.any(np.isnan(update_val)):
raise ValueError("debug value of %s contains nans" %
update.name)
check()
if dont_you_fucking_dare_touch_the_generator:
for param in model.generator.get_params():
assert param not in updates
with log_timing(log, 'Compiling sgd_update'):
return function(theano_args,
updates=updates,
name='sgd_update',
on_unused_input='ignore',
mode=self.theano_function_mode)
self.d_func = get_func(1,
0,
dont_you_fucking_dare_touch_the_generator=True)
self.g_func = get_func(0, 1)
def main():
parser = argparse.ArgumentParser(description='Pylearn2 lab.')
parser.add_argument('-s',
'--save',
action='store_true',
help='Save the resulting images')
parser.add_argument(
'-q',
'--quit',
action='store_true',
help='Quit after plotting instead of dropping into IPython')
parser.add_argument('directory',
type=str,
help='Which results directory to use')
args = parser.parse_args()
# OLD
#config_file_path = '/home/jason/s/deep_learning/pylearn/pred_net.yaml'
#train = yaml_parse.load_path(config_file_path)
#train = serial.load_train_file(config_file_path)
#result_prefix = '/home/jason/s/pylearn2/pylearn2/pred/results/'
result_prefix = '/u/yosinski/s/galatea/fish/results/'
result_dir = os.path.join(result_prefix, args.directory)
print 'loading train object...'
#train = serial.load_train_file(os.path.join(result_dir, 'pred_net.yaml'))
train = serial.load_train_file(os.path.join(result_dir, 'model.yaml'))
print 'loading saved model...'
#model = serial.load(os.path.join(result_dir, 'pred_net.pkl'))
model = serial.load(os.path.join(result_dir, 'model.pkl'))
print 'done.'
print 'model was trained on:'
print model.dataset_yaml_src
if train.algorithm.cost is not None:
data_specs = train.algorithm.cost.get_data_specs(model)
else:
data_specs = train.model.get_default_cost().get_data_specs(train.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
flat_data_specs = (CompositeSpace(space_tuple), source_tuple)
num_frames = model.num_frames
num_batches = 100
batch_size = train.algorithm.batch_size if train.algorithm.batch_size else 20 * num_frames
train_dataset = train.dataset
valid_dataset = train.algorithm.monitoring_dataset['valid']
rng = train.algorithm.rng
if not is_stochastic(train.algorithm.train_iteration_mode):
rng = None
train_iterator = train_dataset.iterator(
mode=train.algorithm.train_iteration_mode,
batch_size=batch_size,
data_specs=flat_data_specs,
return_tuple=True,
rng=rng,
num_batches=num_batches * 10)
valid_iterator = valid_dataset.iterator(
mode=train.algorithm.train_iteration_mode,
batch_size=batch_size,
data_specs=flat_data_specs,
return_tuple=True, # No rng override
num_batches=num_batches * 10)
train_batches = [train_iterator.next() for ii in range(num_batches)]
valid_batches = [valid_iterator.next() for ii in range(num_batches)]
print 'got batches with shape:'
for dat in train_batches[0]:
print ' ', dat.shape
#########################
# Plot costs
#########################
# Plot costs over time
ch_train_objective = model.monitor.channels['train_objective']
ch_valid_objective = model.monitor.channels['valid_objective']
x_vals = ch_train_objective.epoch_record
x_label = 'epoch'
plot(x_vals, ch_train_objective.val_record, 'b-')
plot(x_vals, ch_valid_objective.val_record, 'r-')
legend(('train', 'valid'))
if args.save:
savefig(os.path.join(result_dir, 'costs_lin.png'))
savefig(os.path.join(result_dir, 'costs_lin.pdf'))
if args.save:
gca().set_yscale('log')
savefig(os.path.join(result_dir, 'costs_log.png'))
savefig(os.path.join(result_dir, 'costs_log.pdf'))
gca().set_yscale('linear')
#########################
# Compute some accuracies
#########################
try:
model.fns.feat_to_compout
except:
model.redo_theano()
all_acc_id = []
all_xy_errs = []
print 'Training set:'
print ' acc_id\tx_err\ty_err'
for bb, batch in enumerate(train_batches):
feat, ids, xy = batch
idsN_floatX = array(ids.argmax(1), dtype=theano.config.floatX)
acc_id = model.fns.wiskott_id_accuracy(feat, idsN_floatX)
all_acc_id.append(acc_id)
xy_errs = model.fns.wiskott_xy_errors(feat, xy[:, 0:2])
all_xy_errs.append(xy_errs)
# Old numpy way
#ids_hat,xy_hat = model.fns.feat_to_idxy(feat)
#idx_true = np.where( ids == 1 )[1]
#idx_hat = np.where(np.sign(ids_hat.T - ids_hat.max(1)).T + 1)[1]
#n_correct += (idx_true == idx_hat).sum()
#n_total += len(idx_true)
print '%2d:\t%g,\t%g,\t%g' % (bb, acc_id, xy_errs[0], xy_errs[1])
#########################
# Embed
#########################
if not args.quit:
# Start shell
ipshell()
print 'done.'
def __init__(self, data_callbacks, data_specs):
self.data_callbacks = data_callbacks
self.data_specs = data_specs
self._mapping = DataSpecsMapping(data_specs)
def setup_impl(self, model, dataset, algorithm):
cost = algorithm.cost
root = model.get_param_vector()
dim = root.size
rng = self.rng
points = rng.randn(self.num_points, self.num_basis_vectors)
points = points.astype(root.dtype)
points *= self.scale
if self.include_root:
points[0, :] = 0.
if not hasattr(self, 'cost_fn'):
# Cargo cult all the Pascal bullshit needed to evaluate the f*****g cost function now
# =======================================
data_specs = cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s[%s]' % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name,
batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = cost.expr(model, nested_args,
** fixed_var_descr.fixed_vars)
# End cargo culting
# ======================
print "Compiling cost function..."
cost_fn = function(theano_args, cost_value)
self.cost_fn = cost_fn
else:
cost_fn = self.cost_fn
cost_values = np.zeros(self.num_points)
data = list(dataset.get_batch_design(self.batch_size,
include_labels=True))
from pylearn2.utils.one_hot import one_hot
data[1] = one_hot(data[1])
if self.method == 'gaussian':
basis = rng.normal(dim, self.num_basis_vectors).astype(root.dtype)
elif self.method == 'element':
basis = np.zeros((dim, self.num_basis_vectors)).astype(root.dtype)
for i in xrange(self.num_basis_vectors):
basis[rng.randint(dim), i] = 1.
elif self.method == 'gradient':
if not hasattr(self, 'grad_fn'):
self.grad_fn = function(theano_args, grad(cost_value, model.get_params()))
grad_fn = self.grad_fn
basis = np.zeros((dim, self.num_basis_vectors)).astype(root.dtype)
for i in xrange(self.num_basis_vectors):
ipt = list(dataset.get_batch_design(1, include_labels=True))
label = ipt[1]
assert label.size == 1
label = label[0]
one_hot = np.zeros((1, 10,),dtype='float32')
one_hot[0, label] = 1
ipt[1] = one_hot
g = grad_fn(*ipt)
basis[:,i] = np.concatenate([e.reshape(e.size) for e in g], axis=0)
else:
assert False
basis /= np.sqrt(np.square(basis).sum(axis=0))
# Orthogonalize basis
for i in xrange(self.num_basis_vectors):
v = basis[:,i ].copy()
for j in xrange(i - 1):
u = basis[:, j].copy()
v -= np.dot(u, v) * u
norm = np.sqrt(np.square(v).sum())
assert norm > 1e-4
v /= norm
basis[:,i] = v
for i in xrange(self.num_points):
print "Evaluating cost at point ", i
point = points[i, :]
full_point = root + np.dot(basis, point)
model.set_param_vector(full_point)
cost_values[i] = cost_fn(*data)
print cost_values[i]
from pylearn2.utils import sharedX
import theano.tensor as T
print "!!!!!!!! FITTING THE QUADRATIC FUNCTION !!!!!!!!!!!!!!!!!!!"
if not hasattr(self, 'fit_quad'):
points = sharedX(points)
#from theano import config
#config.compute_test_value = 'raise'
cost_values = sharedX(cost_values)
A = sharedX(np.zeros((self.num_basis_vectors, self.num_basis_vectors)))
if self.psd:
mat = T.dot(A.T, A)
else:
mat = A
b = sharedX(np.zeros(self.num_basis_vectors))
c = sharedX(0.)
half_quad = T.dot(points, mat)
quad = (points * half_quad).sum(axis=1)
lin = T.dot(points, b)
pred = quad + lin + c
from pylearn2.optimization.batch_gradient_descent import BatchGradientDescent
mse = T.square(pred - cost_values).mean()
mae = abs(pred - cost_values).mean()
obj = locals()[self.fitting_cost]
fit_quad = BatchGradientDescent(obj, params = [A, b, c],
max_iter = self.num_basis_vectors ** 2,
verbose = 3, tol = None,
init_alpha = None, min_init_alpha = 1e-7,
reset_alpha = False, conjugate = True,
reset_conjugate = False,
line_search_mode = 'exhaustive')
self.fit_quad = fit_quad
self.A = A
self.b = b
self.c = c
self.points = points
self.cost_values = cost_values
else:
self.A.set_value(.001 * np.identity(self.A.get_value().shape[0], dtype=self.A.dtype))
self.b.set_value(self.b.get_value() * 0.)
self.c.set_value(self.c.get_value() * 0.)
self.points.set_value(points)
self.cost_values.set_value(cost_values.astype(self.cost_values.dtype))
self.fit_quad.minimize()
print "!!!!!!!!!!!!! FINDING ITS MINIMUM !!!!!!!!!!!!!!!!!!!!!!!!!!!"
if self.use_solver:
if self.psd:
Av = self.A.get_value()
mat_v = np.dot(Av.T, Av)
else:
mat_v = self.A.get_value()
bv = self.b.get_value()
# minimize for x^T A x + b^T x + c
# -> solve 2 A x + b = 0
# Ax = - b / 2
print "********** mat_v", mat_v.min(), mat_v.max()
x, ignored_residuals, ignored_rank, ignored_singular_values = np.linalg.lstsq(mat_v, - 0.5 * bv)
print "********** soln: ", x.min(), x.mean(), x.max()
print "********** SVs: ", ignored_singular_values.min(), ignored_singular_values.max()
assert x.ndim == 1, x.shape
prod = np.dot(basis, x)
norm = np.sqrt(np.square(prod).sum())
print "*************** Moving params by ",norm
vector = root + prod
model.set_param_vector(vector)
else: # use minimizer
if not hasattr(self, 'fit_params'):
self.vector = sharedX(points.get_value().mean(axis=0))
vector = self.vector
obj = T.dot(T.dot(mat, vector), vector) + T.dot(b, vector)
def constrain(d):
assert vector in d
n = d[vector]
norm = T.sqrt(T.square(n).sum())
desired_norm = T.clip(norm, 0., self.max_jump_norm)
d[vector] = n * desired_norm / norm
self.fit_params = BatchGradientDescent(obj, params=[vector],
max_iter = self.num_basis_vectors,
verbose = 3, tol=None,
param_constrainers = [constrain],
init_alpha = None, min_init_alpha = 1e-3,
reset_alpha=False, conjugate=True, reset_conjugate=False,
line_search_mode='exhaustive')
else:
self.vector.set_value(points.mean(axis=0).astype(self.vector.dtype))
self.fit_params.minimize()
model.set_param_vector(root + np.dot(basis , self.vector.get_value()))
class Monitor(object):
"""
A class for monitoring Models while they are being trained.
A monitor object records the number of minibatches and number of
examples the model has trained, as well as any number of "channels"
that track quantities of interest (examples: the objective
function, measures of hidden unit activity, reconstruction error,
sum of squared second derivatives, average norm of the weight
vectors, etc.)
Parameters
----------
model : `pylearn2.models.model.Model`
"""
def __init__(self, model):
self.training_succeeded = False
self.model = model
self.channels = OrderedDict()
self._num_batches_seen = 0
self._examples_seen = 0
self._epochs_seen = 0
self._datasets = []
self._iteration_mode = []
self._batch_size = []
self._num_batches = []
self._dirty = True
self._rng_seed = []
self.names_to_del = ['theano_function_mode']
self.t0 = time.time()
self.theano_function_mode = None
# Initialize self._nested_data_specs, self._data_specs_mapping,
# and self._flat_data_specs
self._build_data_specs()
def _build_data_specs(self):
"""
Computes a nested data_specs for input and all channels
Also computes the mapping to flatten it. This function is
called from redo_theano.
"""
# Ask the model what it needs
m_space, m_source = self.model.get_monitoring_data_specs()
input_spaces = [m_space]
input_sources = [m_source]
for channel in self.channels.values():
space = channel.data_specs[0]
assert isinstance(space, Space)
input_spaces.append(space)
input_sources.append(channel.data_specs[1])
nested_space = CompositeSpace(input_spaces)
nested_source = tuple(input_sources)
self._nested_data_specs = (nested_space, nested_source)
self._data_specs_mapping = DataSpecsMapping(self._nested_data_specs)
flat_space = self._data_specs_mapping.flatten(nested_space,
return_tuple=True)
flat_source = self._data_specs_mapping.flatten(nested_source,
return_tuple=True)
self._flat_data_specs = (CompositeSpace(flat_space), flat_source)
def set_theano_function_mode(self, mode):
"""
.. todo::
WRITEME
Parameters
----------
mode : theano.compile.Mode
Theano functions for the monitoring channels will be
compiled and run using this mode.
"""
if self.theano_function_mode != mode:
self._dirty = True
self.theano_function_mode = mode
def add_dataset(self, dataset, mode='sequential', batch_size=None,
num_batches=None, seed=None):
"""
Determines the data used to calculate the values of each channel.
Parameters
----------
dataset : object
A `pylearn2.datasets.Dataset` object.
mode : str or object, optional
Iteration mode; see the docstring of the `iterator` method
on `pylearn2.datasets.Dataset` for details.
batch_size : int, optional
The size of an individual batch. Optional if `mode` is
'sequential' and `num_batches` is specified (batch size
will be calculated based on full dataset size).
num_batches : int, optional
The total number of batches. Unnecessary if `mode` is
'sequential' and `batch_size` is specified (number of
batches will be calculated based on full dataset size).
seed : int, optional
Optional. The seed to be used for random iteration modes.
"""
# The user can ommit using lists if only one dataset is set
if not isinstance(dataset, list):
dataset = [dataset]
if not isinstance(mode, list):
mode = [mode]
if not isinstance(batch_size, list):
batch_size = [batch_size]
if not isinstance(num_batches, list):
num_batches = [num_batches]
if seed is None:
seed = [None] * len(dataset)
if not isinstance(seed, list):
seed = [seed]
if len(mode) != len(dataset):
raise ValueError("Received " + str(len(dataset)) +
" dataset but " + str(len(mode)) + " modes.")
if any([len(l) != len(dataset) for l in [batch_size, seed]]):
raise ValueError("make sure each dataset has its iteration " +
"batch size and number of batches.")
for (d, m, b, n, sd) in safe_izip(dataset, mode, batch_size,
num_batches, seed):
try:
it = d.iterator(mode=m,
batch_size=b,
num_batches=n,
data_specs=self._flat_data_specs,
return_tuple=True,
rng=sd)
except ValueError as exc:
reraise_as(ValueError("invalid iteration parameters in " +
"Monitor.add_dataset: " + str(exc)))
if it.stochastic:
# Must be a seed, not a random number generator. If it were a
# random number generator, different iterators using it would
# update its state, so we would not get the same iterator
# each time. Also, must not be None, because this makes the
# iterator pick a seed based on the clock
if sd is None:
raise TypeError("Monitor requires a seed when using " +
"stochastic iteration modes.")
if not isinstance(sd, (list, tuple, int)):
raise TypeError("Monitor requires a seed (not a random " +
"number generator) when using " +
"stochastic iteration modes.")
else:
# The iterator should catch this, but let's double-check
assert sd is None
if not d in self._datasets:
self._datasets.append(d)
self._iteration_mode.append(m)
self._batch_size.append(b)
self._num_batches.append(n)
self._rng_seed.append(sd)
def __call__(self):
"""
Runs the model on the monitoring dataset in order to add one
data point to each of the channels.
"""
# If the channels have changed at all, we need to recompile the theano
# functions used to compute them
if self._dirty:
self.redo_theano()
datasets = self._datasets
# Set all channels' val_shared to 0
self.begin_record_entry()
for d, i, b, n, a, sd, ne in safe_izip(datasets,
self._iteration_mode,
self._batch_size,
self._num_batches,
self.accum,
self._rng_seed,
self.num_examples):
if isinstance(d, basestring):
d = yaml_parse.load(d)
raise NotImplementedError()
# need to put d back into self._datasets
myiterator = d.iterator(mode=i,
batch_size=b,
num_batches=n,
data_specs=self._flat_data_specs,
return_tuple=True,
rng=sd)
# If self._flat_data_specs is empty, no channel needs data,
# so we do not need to call the iterator in order to average
# the monitored values across different batches, we only
# have to call them once.
if len(self._flat_data_specs[1]) == 0:
X = ()
self.run_prereqs(X, d)
a(*X)
else:
actual_ne = 0
for X in myiterator:
# X is a flat (not nested) tuple
self.run_prereqs(X, d)
a(*X)
actual_ne += self._flat_data_specs[0].np_batch_size(X)
# end for X
if actual_ne != ne:
raise RuntimeError("At compile time, your iterator said "
"it had %d examples total, but at "
"runtime it gave us %d." %
(ne, actual_ne))
# end for d
log.info("Monitoring step:")
log.info("\tEpochs seen: %d" % self._epochs_seen)
log.info("\tBatches seen: %d" % self._num_batches_seen)
log.info("\tExamples seen: %d" % self._examples_seen)
t = time.time() - self.t0
for channel_name in sorted(self.channels.keys(),
key=number_aware_alphabetical_key):
channel = self.channels[channel_name]
channel.time_record.append(t)
channel.batch_record.append(self._num_batches_seen)
channel.example_record.append(self._examples_seen)
channel.epoch_record.append(self._epochs_seen)
val = channel.val_shared.get_value()
channel.val_record.append(val)
# TODO: use logging infrastructure so that user can configure
# formatting
if abs(val) < 1e4:
val_str = str(val)
else:
val_str = '%.3e' % val
log.info("\t%s: %s" % (channel_name, val_str))
def run_prereqs(self, data, dataset):
"""
Runs all "prerequistie functions" on a batch of data. Always
called right before computing the monitoring channels on that
batch.
Parameters
----------
data : tuple or Variable
a member of the Space used as input to the monitoring
functions
dataset : Dataset
the Dataset the data was drawn from
"""
if dataset not in self.prereqs:
return
for prereq in self.prereqs[dataset]:
prereq(*data)
def get_batches_seen(self):
"""
Returns the number of batches the model has learned on
(assuming that the learning code has been calling
Monitor.report_batch correctly).
"""
return self._num_batches_seen
def get_epochs_seen(self):
"""
.. todo::
WRITEME
Returns
-------
epochs_seen : int
The number of epochs the model has been trained on.
One "epoch" is one pass through Dataset.iterator.
"""
return self._epochs_seen
def get_examples_seen(self):
"""
.. todo::
WRITEME
Returns
-------
examples_seen : int
The number of examples the model has learned on (assuming
that the learning code has been calling Monitor.report_batch
correctly)
"""
return self._examples_seen
def report_batch(self, num_examples):
"""
Call this whenever the model has learned on another batch of
examples. Report how many examples were learned on.
Parameters
----------
num_examples : int
The number of examples learned on in this minibatch.
"""
self._examples_seen += num_examples
self._num_batches_seen += 1
def report_epoch(self):
"""
Call this whenever the model has completed another "epoch" of
learning. We regard one pass through Dataset.iterator as one
epoch.
"""
self._epochs_seen += 1
def redo_theano(self):
"""
Recompiles Theano functions used by this monitor.
This is called any time we need to evaluate the channels and
the channel definitions have changed since last we called it,
or if the theano functions are unavailable for any other reason
(first time they are needed after construction or
deserialization, etc.)
All channels are compiled as part of the same theano function
so that the theano optimizations can eliminate subexpressions
that are shared between multiple channels.
"""
self._dirty = False
# Recompute the data specs, since the channels may have changed.
self._build_data_specs()
init_names = dir(self)
self.prereqs = OrderedDict()
for channel in self.channels.values():
if channel.prereqs is not None:
dataset = channel.dataset
if dataset not in self.prereqs:
self.prereqs[dataset] = []
prereqs = self.prereqs[dataset]
for prereq in channel.prereqs:
if prereq not in prereqs:
prereqs.append(prereq)
updates = OrderedDict()
for channel in self.channels.values():
updates[channel.val_shared] = np.cast[config.floatX](0.0)
with log_timing(log, "compiling begin_record_entry"):
self.begin_record_entry = function(
inputs=[],
updates=updates,
mode=self.theano_function_mode,
name='Monitor.begin_record_entry'
)
updates = OrderedDict()
givens = OrderedDict()
# Get the appropriate kind of theano variable to represent the data
# the model acts on
batch_names = ['monitoring_%s' % s for s in self._flat_data_specs[1]]
theano_args = self._flat_data_specs[0].make_theano_batch(batch_names)
# Get a symbolic expression of the batch size
# We do it here, rather than for each channel, because channels with an
# empty data_specs do not use data, and are unable to extract the batch
# size. The case where the whole data specs is empty is not supported.
batch_size = self._flat_data_specs[0].batch_size(theano_args)
# Also get a nested representation, for joint iteration
# with each of channel.graph_input
nested_theano_args = self._data_specs_mapping.nest(theano_args)
if not isinstance(nested_theano_args, tuple):
nested_theano_args = (nested_theano_args,)
assert len(nested_theano_args) == (len(self.channels) + 1)
log.info('Monitored channels: ')
for key in sorted(self.channels.keys()):
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling monitor including ' +
'channel ' + key + '\n')
log.info('\t%s' % key)
it = [d.iterator(mode=i, num_batches=n, batch_size=b,
data_specs=self._flat_data_specs,
return_tuple=True)
for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
self._num_batches, self._batch_size)]
self.num_examples = [np.cast[config.floatX](float(i.num_examples))
for i in it]
self.num_examples = [float(i.num_examples) for i in it]
givens = [OrderedDict() for d in self._datasets]
updates = [OrderedDict() for d in self._datasets]
for i, channel in enumerate(self.channels.values()):
index = self._datasets.index(channel.dataset)
d = self._datasets[index]
g = givens[index]
inv_cur_num_examples = as_floatX(1./self.num_examples[index])
u = updates[index]
# Flatten channel.graph_input and the appropriate part of
# nested_theano_args, to iterate jointly over them.
c_mapping = DataSpecsMapping(channel.data_specs)
channel_inputs = c_mapping.flatten(channel.graph_input,
return_tuple=True)
inputs = c_mapping.flatten(nested_theano_args[i + 1],
return_tuple=True)
for (channel_X, X) in safe_izip(channel_inputs, inputs):
assert channel_X not in g or g[channel_X] is X
assert channel_X.type == X.type, (channel_X.type, X.type)
g[channel_X] = X
if batch_size == 0:
# No channel does need any data, so there is not need to
# average results, and we will call the accum functions only
# once.
# TODO: better handling of channels not needing data when
# some other channels need data.
assert len(self._flat_data_specs[1]) == 0
val = channel.val
else:
if n == 0:
raise ValueError("Iterating over 0 examples results in " +
"divide by 0")
val = (channel.val * T.cast(batch_size, config.floatX)
* inv_cur_num_examples)
u[channel.val_shared] = channel.val_shared + val
with log_timing(log, "Compiling accum"):
# Check type of update expressions
for up in updates:
for key in up:
if key.dtype != up[key].dtype:
raise TypeError('Monitoring channel shared variable ' +
key.name + ' has dtype ' + key.dtype +
' but is driven by an expression ' +
'with type ' + up[key].dtype)
self.accum = []
for idx, packed in enumerate(safe_izip(givens, updates)):
g, u = packed
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
for elem in g:
mode.record.handle_line('g key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('g val ' +
var_descriptor(g[elem]) + '\n')
for elem in u:
mode.record.handle_line('u key ' +
var_descriptor(elem) + '\n')
mode.record.handle_line('u val ' +
var_descriptor(u[elem]) + '\n')
function_name = 'Monitor.accum[%d]' % idx
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('compiling supervised accum\n')
# Some channels may not depend on the data, ie, they might just
# monitor the model parameters, or some shared variable updated
# by the training algorithm, so we need to ignore the unused
# input error
self.accum.append(function(theano_args,
givens=g,
updates=u,
mode=self.theano_function_mode,
name=function_name))
for a in self.accum:
if mode is not None and hasattr(mode, 'record'):
for elem in a.maker.fgraph.outputs:
mode.record.handle_line('accum output ' +
var_descriptor(elem) + '\n')
log.info("graph size: %d" % len(a.maker.fgraph.toposort()))
final_names = dir(self)
self.register_names_to_del([name for name in final_names
if name not in init_names])
def register_names_to_del(self, names):
"""
Register names of fields that should be deleted before pickling.
Parameters
----------
names : list
A list of attribute names as strings.
"""
for name in names:
if name not in self.names_to_del:
self.names_to_del.append(name)
def __getstate__(self):
"""
In order to avoid pickling a copy of the dataset whenever a
monitor is saved, the __getstate__ method replaces the dataset
field with the dataset's yaml source. This is not a perfect
solution because it won't work with job resuming, which would
require saving the state of the dataset's random number
generator.
Like in the Model class, we also need to avoid saving any
Theano functions, so we delete everything that can be
regenerated with `redo_theano` by deleting the fields in
`self.names_to_del`
"""
# Patch old pickled monitors
if not hasattr(self, '_datasets'):
self._datasets = [self._dataset]
del self._dataset
temp = self._datasets
if self._datasets:
self._datasets = []
for dataset in temp:
if isinstance(dataset, basestring):
self._datasets.append(dataset)
else:
try:
self._datasets.append(dataset.yaml_src)
except AttributeError:
warnings.warn('Trained model saved without ' +
'indicating yaml_src')
d = copy.copy(self.__dict__)
self._datasets = temp
for name in self.names_to_del:
if name in d:
del d[name]
return d
def __setstate__(self, d):
"""
Sets the object to have the state described by `d`.
Parameters
----------
d : dict
A dictionary mapping string names of fields to values for
these fields.
"""
# patch old pkl files
if '_dataset' in d:
d['_datasets'] = [d['_dataset']]
del d['_dataset']
self.__dict__.update(d)
def add_channel(self, name, ipt, val, dataset=None, prereqs=None,
data_specs=None):
"""
Asks the monitor to start tracking a new value. Can be called
even after the monitor is already in use.
Parameters
----------
name : str
The display name in the monitor.
ipt : tensor_like
The symbolic tensor which should be clamped to the data.
(or a list/tuple containing symbolic tensors, following the
data_specs)
val : tensor_like
The value (function of `ipt`) to be tracked.
dataset : pylearn2.datasets.Dataset
Which dataset to compute this channel on
prereqs : list of callables that take a list of numpy tensors
Each prereq must be called exactly once per each new batch
of data drawn *from dataset* before the channel value is
computed if two channels provide a prereq with exactly the
same id, that prereq will only be called once
data_specs : (space, source) pair
Identifies the order, format and semantics of ipt
"""
if isinstance(val, (float, int, long)):
val = np.cast[theano.config.floatX](val)
val = T.as_tensor_variable(val)
if data_specs is None:
warnings.warn("parameter 'data_specs' should be provided when " +
"calling add_channel. We will build a default one.",
stacklevel=2)
if isinstance(ipt, list):
ipt = tuple(ipt)
if ipt is not None and not isinstance(ipt, tuple):
ipt = (ipt,)
if ipt is None:
data_specs = (NullSpace(), '')
elif len(ipt) == 0:
data_specs = (CompositeSpace([]), ())
elif hasattr(dataset, 'get_data_specs'):
dataset_space, dataset_source = dataset.get_data_specs()
if (len(ipt) == 1 and
dataset_source is not None and
(not isinstance(dataset_source, tuple) or
len(dataset_source) == 1) and
'features' in dataset_source):
data_specs = (dataset_space, dataset_source)
elif (len(ipt) == 2 and
dataset_source == ('features', 'targets')):
data_specs = (dataset_space, dataset_source)
else:
raise ValueError("Cannot infer default data_specs for " +
"the following input points and " +
"dataset: ipt = %s, dataset = %s"
% (ipt, dataset))
data_specs[0].validate(ipt)
mapping = DataSpecsMapping(data_specs)
flat_ipt = mapping.flatten(ipt)
if not isinstance(flat_ipt, tuple):
flat_ipt = (flat_ipt,)
inputs = theano.gof.graph.inputs([val])
for elem in inputs:
if not hasattr(elem, 'get_value') and \
not isinstance(elem, theano.gof.graph.Constant):
if elem not in flat_ipt:
raise ValueError("Unspecified input: " + str(elem) +
". This may be due to an incorrect " +
"implementation of a cost's " +
"get_data_specs() method, or of a " +
"model's get_monitoring_data_specs() " +
"method.")
mode = self.theano_function_mode
if mode is not None and hasattr(mode, 'record'):
mode.record.handle_line('Adding monitor channel '+name+'\n')
assert isinstance(flat_ipt, tuple)
if len(flat_ipt) != 1:
for elem in flat_ipt:
mode.record.handle_line('Includes input var ' +
var_descriptor(elem) + '\n')
else:
mode.record.handle_line(name + ' input var is ' +
var_descriptor(flat_ipt[0]) + '\n')
mode.record.handle_line('channel ' + name + ' is ' +
var_descriptor(val) + '\n')
if dataset is None:
if len(self._datasets) == 1:
dataset = self._datasets[0]
elif len(self._datasets) == 0:
raise ValueError(_err_no_data)
else:
raise ValueError(_err_ambig_data)
try:
self._datasets.index(dataset)
except ValueError:
reraise_as(ValueError("The dataset specified is not one of the " +
"monitor's datasets"))
if name in self.channels:
raise ValueError("Tried to create the same channel twice (%s)" %
name)
self.channels[name] = MonitorChannel(ipt, val, name, data_specs,
dataset, prereqs)
self._dirty = True
def _sanity_check(self):
"""
Sometimes we serialize models and then load them somewhere else
but still try to use their Monitor, and the Monitor is in a
mangled state. I've added some calls to _sanity_check to try to
catch when that happens. Not sure what to do for a long term
fix. I think it requires making theano graphs serializable
first.
"""
for name in self.channels:
channel = self.channels[name]
assert hasattr(channel, 'prereqs')
@classmethod
def get_monitor(cls, model):
"""
Returns a model's monitor. If the model doesn't have a monitor
yet, installs one and returns that.
Parameters
----------
model : object
An object that implements the `Model` interface specified
in `pylearn2.models`.
"""
if hasattr(model, 'monitor'):
rval = model.monitor
rval._sanity_check()
else:
rval = Monitor(model)
model.monitor = rval
return rval
# TODO: find out if this method is used anywhere, remove if not.
@property
def batch_size(self):
"""
.. todo::
WRITEME
Returns
-------
batch_size : int
The size of the batches used for monitoring
"""
return self._batch_size
# TODO: find out if this method is used anywhere, remove if not.
@property
def num_batches(self):
"""
.. todo::
WRITEME
Returns
-------
num_batches : int
The number of batches used for monitoring
"""
return self._num_batches
def setup(self, dataset, cost, batch_size, num_batches=None,
extra_costs=None, mode='sequential', obj_prereqs=None,
cost_monitoring_args=None):
"""
Sets up the monitor for a cost minimization problem.
Adds channels defined by both the model and the cost for
the specified dataset(s), as well as a channel called
'objective' defined by the costs' __call__ method.
Parameters
----------
dataset : pylearn2.datasets.Dataset
Dataset or dictionary mapping string names to Datasets.
If string names are used, then for every dataset, each
channel defined by the model or cost will be replicated
with that dataset's name followed by an underscore as the
prefix. For example, if your cost defines a channel called
'misclass', and datasets is
{'train' : train_dataset, 'valid' : valid_dataset},
you will get channels called 'train_misclass' and
'valid_misclass'.
cost : pylearn2.costs.Cost
The cost being optimized by training. The value of the cost
will appear as the `objective` channel. Its
`get_monitoring_channels` method will also be used to
supply other channels.
extra_costs : OrderedDict, optional
A dictionary mapping channel names to Cost objects.
Their value will appear as the specified channel name.
They will also provide more monitoring channels via their
`get_monitoring_channels` method.
obj_prereqs : None, or list of functions
Functions to pass as prerequisites to the `objective` channel.
cost_monitoring_args : dict
Dictionary of kwargs that will be passed to
`cost.get_monitoring_channels()`
(but not for the extra_costs).
"""
if dataset is None:
return
if isinstance(dataset, Dataset):
dataset = {'': dataset}
else:
assert isinstance(dataset, dict)
assert all(isinstance(key, str) for key in dataset)
assert all(isinstance(dataset[key], Dataset) for key in dataset)
if extra_costs is None:
costs = {}
else:
assert isinstance(extra_costs, (OrderedDict, dict))
costs = extra_costs
assert '' not in costs
costs[''] = cost
if cost_monitoring_args is None:
cost_monitoring_args = {}
model = self.model
# Build a composite data_specs containing the specs for all costs,
# then the specs of the model
cost_names = sorted(costs.keys())
spaces = []
sources = []
for c in cost_names:
c_space, c_source = costs[c].get_data_specs(model)
spaces.append(c_space)
sources.append(c_source)
# Ask the model for the data_specs needed
m_space, m_source = model.get_monitoring_data_specs()
spaces.append(m_space)
sources.append(m_source)
nested_space = CompositeSpace(spaces)
nested_sources = tuple(sources)
# Flatten this data_specs, so we build only one symbolic Theano
# variable for each of the unique (space, source) pairs.
mapping = DataSpecsMapping((nested_space, nested_sources))
space_tuple = mapping.flatten(nested_space, return_tuple=True)
source_tuple = mapping.flatten(nested_sources, return_tuple=True)
ipt = tuple(space.make_theano_batch(name='monitor_%s' % source,
batch_size=None)
for (space, source) in safe_zip(space_tuple, source_tuple))
# Build a nested tuple from ipt, to dispatch the appropriate parts
# of the ipt batch to each cost
nested_ipt = mapping.nest(ipt)
custom_channels = {}
for i, cost_name in enumerate(cost_names):
if cost_name == '':
prefix = ''
else:
prefix = cost_name + '_'
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
raw_channels = cost.get_monitoring_channels(model, cost_ipt)
channels = {}
for name in raw_channels:
# We need three things: the value itself (raw_channels[name]),
# the input variables (cost_ipt), and the data_specs for
# these input variables ((spaces[i], sources[i]))
channels[prefix + name] = (raw_channels[name],
cost_ipt,
(spaces[i], sources[i]))
custom_channels.update(channels)
# Use the last inputs from nested_ipt for the model
model_channels = model.get_monitoring_channels(nested_ipt[-1])
channels = {}
for name in model_channels:
# Note: some code used to consider that model_channels[name]
# could be a a (channel, prereqs) pair, this is not supported.
channels[name] = (model_channels[name],
nested_ipt[-1],
(spaces[-1], sources[-1]))
custom_channels.update(channels)
if is_stochastic(mode):
seed = [[2013, 02, 22]]
else:
seed = None
for dataset_name in dataset:
cur_dataset = dataset[dataset_name]
self.add_dataset(dataset=cur_dataset,
mode=mode,
batch_size=batch_size,
num_batches=num_batches,
seed=seed)
if dataset_name == '':
dprefix = ''
else:
dprefix = dataset_name + '_'
# These channel name 'objective' must not vary, since callbacks
# that respond to the values in the monitor use the name to find
# it.
for i, cost_name in enumerate(cost_names):
cost = costs[cost_name]
cost_ipt = nested_ipt[i]
cost_value = cost.expr(model, cost_ipt)
if cost_value is not None:
if cost_name == '':
name = dprefix + 'objective'
prereqs = obj_prereqs
else:
name = dprefix + cost_name
prereqs = None
cost.get_data_specs(model)[0].validate(cost_ipt)
self.add_channel(name=name,
ipt=cost_ipt,
val=cost_value,
data_specs=cost.get_data_specs(model),
dataset=cur_dataset,
prereqs=prereqs)
for key in custom_channels:
val, ipt, data_specs = custom_channels[key]
data_specs[0].validate(ipt)
self.add_channel(name=dprefix + key,
ipt=ipt,
val=val,
data_specs=data_specs,
dataset=cur_dataset)
def setup(self, model, dataset):
"""
Compiles the theano functions needed for the train method.
Parameters
----------
model : a Model instance
dataset : Dataset
"""
if self.cost is None:
self.cost = model.get_default_cost()
inf_params = [param for param in model.get_params() if contains_inf(param.get_value())]
if len(inf_params) > 0:
raise ValueError("These params are Inf: " + str(inf_params))
if any([contains_nan(param.get_value()) for param in model.get_params()]):
nan_params = [param for param in model.get_params() if contains_nan(param.get_value())]
raise ValueError("These params are NaN: " + str(nan_params))
self.model = model
self._synchronize_batch_size(model)
model._test_batch_size = self.batch_size
self.monitor = Monitor.get_monitor(model)
self.monitor._sanity_check()
# test if force batch size and batch size
has_force_batch_size = getattr(model, "force_batch_size", False)
train_dataset_is_uneven = dataset.get_num_examples() % self.batch_size != 0
has_monitoring_datasets = self.monitoring_dataset is not None and self.monitoring_dataset.values() > 0
if has_monitoring_datasets:
monitoring_datasets_are_uneven = any(
d.get_num_examples() % self.batch_size != 0 for d in self.monitoring_dataset.values()
)
else:
monitoring_datasets_are_uneven = False # or True it doesn't matter
if has_force_batch_size and train_dataset_is_uneven and not has_uniform_batch_size(self.train_iteration_mode):
raise ValueError(
"Dataset size is not a multiple of batch size."
"You should set train_iteration_mode (and "
"maybe monitor_iteration_mode) to "
"even_sequential, even_shuffled_sequential or "
"even_batchwise_shuffled_sequential"
)
if (
has_force_batch_size
and has_monitoring_datasets
and monitoring_datasets_are_uneven
and not has_uniform_batch_size(self.monitor_iteration_mode)
):
raise ValueError(
"Dataset size is not a multiple of batch size."
"You should set monitor_iteration_mode to "
"even_sequential, even_shuffled_sequential or "
"even_batchwise_shuffled_sequential"
)
data_specs = self.cost.get_data_specs(self.model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
# Build a flat tuple of Theano Variables, one for each space.
# We want that so that if the same space/source is specified
# more than once in data_specs, only one Theano Variable
# is generated for it, and the corresponding value is passed
# only once to the compiled Theano function.
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = "%s[%s]" % (self.__class__.__name__, source)
arg = space.make_theano_batch(name=name, batch_size=self.batch_size)
theano_args.append(arg)
theano_args = tuple(theano_args)
# Methods of `self.cost` need args to be passed in a format compatible
# with data_specs
nested_args = mapping.nest(theano_args)
fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
self.on_load_batch = fixed_var_descr.on_load_batch
cost_value = self.cost.expr(model, nested_args, **fixed_var_descr.fixed_vars)
if cost_value is not None and cost_value.name is None:
# Concatenate the name of all tensors in theano_args !?
cost_value.name = "objective"
# Set up monitor to model the objective value, learning rate,
# momentum (if applicable), and extra channels defined by
# the cost
learning_rate = self.learning_rate
if self.monitoring_dataset is not None:
if self.monitoring_batch_size is None and self.monitoring_batches is None:
self.monitoring_batch_size = self.batch_size
self.monitoring_batches = self.batches_per_iter
self.monitor.setup(
dataset=self.monitoring_dataset,
cost=self.cost,
batch_size=self.monitoring_batch_size,
num_batches=self.monitoring_batches,
extra_costs=self.monitoring_costs,
mode=self.monitor_iteration_mode,
)
dataset_name = self.monitoring_dataset.keys()[0]
monitoring_dataset = self.monitoring_dataset[dataset_name]
# TODO: have Monitor support non-data-dependent channels
self.monitor.add_channel(
name="learning_rate",
ipt=None,
val=learning_rate,
data_specs=(NullSpace(), ""),
dataset=monitoring_dataset,
)
if self.learning_rule:
self.learning_rule.add_channels_to_monitor(self.monitor, monitoring_dataset)
params = list(model.get_params())
assert len(params) > 0
for i, param in enumerate(params):
if param.name is None:
param.name = "sgd_params[%d]" % i
grads, updates = self.cost.get_gradients(model, nested_args, **fixed_var_descr.fixed_vars)
if not isinstance(grads, OrderedDict):
raise TypeError(
str(type(self.cost))
+ ".get_gradients returned "
+ "something with"
+ str(type(grads))
+ "as its "
+ "first member. Expected OrderedDict."
)
for param in grads:
assert param in params
for param in params:
assert param in grads
for param in grads:
if grads[param].name is None and cost_value is not None:
grads[param].name = "grad(%(costname)s, %(paramname)s)" % {
"costname": cost_value.name,
"paramname": param.name,
}
assert grads[param].dtype == param.dtype
lr_scalers = model.get_lr_scalers()
for key in lr_scalers:
if key not in params:
raise ValueError(
"Tried to scale the learning rate on " + str(key) + " which is not an optimization parameter."
)
log.info("Parameter and initial learning rate summary:")
for param in params:
param_name = param.name
if param_name is None:
param_name = "anon_param"
lr = learning_rate.get_value() * lr_scalers.get(param, 1.0)
log.info("\t" + param_name + ": " + str(lr))
if self.learning_rule:
updates.update(self.learning_rule.get_updates(learning_rate, grads, lr_scalers))
else:
# Use standard SGD updates with fixed learning rate.
updates.update(
dict(
safe_zip(
params, [param - learning_rate * lr_scalers.get(param, 1.0) * grads[param] for param in params]
)
)
)
for param in params:
if updates[param].name is None:
updates[param].name = "sgd_update(" + param.name + ")"
model.modify_updates(updates)
for param in params:
update = updates[param]
if update.name is None:
update.name = "censor(sgd_update(" + param.name + "))"
for update_val in get_debug_values(update):
if contains_inf(update_val):
raise ValueError("debug value of %s contains infs" % update.name)
if contains_nan(update_val):
raise ValueError("debug value of %s contains nans" % update.name)
with log_timing(log, "Compiling sgd_update"):
self.sgd_update = function(
theano_args,
updates=updates,
name="sgd_update",
on_unused_input="ignore",
mode=self.theano_function_mode,
)
self.params = params
