class TestBidirectionalStack(unittest.TestCase):
def setUp(self):
prototype = SimpleRecurrent(dim=3, activation=Tanh())
self.layers = [
Bidirectional(weights_init=Orthogonal(), prototype=prototype)
for _ in range(3)
]
self.stack = RecurrentStack(self.layers)
for fork in self.stack.forks:
fork.weights_init = Identity(1)
fork.biases_init = Constant(0)
self.stack.initialize()
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))), dtype=theano.config.floatX)
self.x_val = (numpy.ones(
(24, 4, 3), dtype=theano.config.floatX) * self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
self.mask_val[12:24, 3] = 0
def test_steps(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
calc_stack_layers = [
theano.function([x, mask],
self.stack.apply(x, mask=mask)[i])
for i in range(len(self.layers))
]
stack_layers = [
f(self.x_val, self.mask_val) for f in calc_stack_layers
]
h_val = self.x_val
for stack_layer_value, bidir_net in zip(stack_layers, self.layers):
calc = theano.function([x, mask], bidir_net.apply(x, mask=mask))
simple_layer_value = calc(h_val, self.mask_val)
assert_allclose(stack_layer_value, simple_layer_value, rtol=1e-04)
h_val = simple_layer_value[..., :3]
def test_dims(self):
self.assertEqual(self.stack.get_dim("inputs"), 3)
for i in range(len(self.layers)):
state_name = self.stack.suffix("states", i)
self.assertEqual(self.stack.get_dim(state_name), 6)
class TestBidirectionalStack(unittest.TestCase):
def setUp(self):
prototype = SimpleRecurrent(dim=3, activation=Tanh())
self.layers = [
Bidirectional(weights_init=Orthogonal(), prototype=prototype)
for _ in range(3)]
self.stack = RecurrentStack(self.layers)
for fork in self.stack.forks:
fork.weights_init = Identity(1)
fork.biases_init = Constant(0)
self.stack.initialize()
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))),
dtype=theano.config.floatX)
self.x_val = (numpy.ones((24, 4, 3), dtype=theano.config.floatX) *
self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
self.mask_val[12:24, 3] = 0
def test_steps(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
calc_stack_layers = [
theano.function([x, mask], self.stack.apply(x, mask=mask)[i])
for i in range(len(self.layers))]
stack_layers = [
f(self.x_val, self.mask_val) for f in calc_stack_layers]
h_val = self.x_val
for stack_layer_value, bidir_net in zip(stack_layers, self.layers):
calc = theano.function([x, mask], bidir_net.apply(x, mask=mask))
simple_layer_value = calc(h_val, self.mask_val)
assert_allclose(stack_layer_value, simple_layer_value, rtol=1e-04)
h_val = simple_layer_value[..., :3]
def test_dims(self):
self.assertEqual(self.stack.get_dim("inputs"), 3)
for i in range(len(self.layers)):
state_name = self.stack.suffix("states", i)
self.assertEqual(self.stack.get_dim(state_name), 6)
def test_suffix(self):
# level >= 0 !!
level1, = numpy.random.randint(1, 150, size=(1, ))
# name1 != "mask" !!
name1 = "somepart"
test_cases = [("mask", level1, "mask"), ("{name}", 0, "{name}"),
("{name}", level1, "{name}{sep}{level}")]
for _name, level, _expected_result in test_cases:
name = _name.format(name=name1,
level=level1,
sep=RECURRENTSTACK_SEPARATOR)
expected_result = _expected_result.format(
name=name1, level=level1, sep=RECURRENTSTACK_SEPARATOR)
resut = RecurrentStack.suffix(name, level)
assert resut == expected_result, "expected suffix(\"{}\",{}) -> \"{}\" got \"{}\"".format(
name, level, expected_result, resut)
def test_suffix(self):
# level >= 0 !!
level1, = numpy.random.randint(1, 150, size=(1,))
# name1 != "mask" !!
name1 = "somepart"
test_cases = [
("mask", level1, "mask"),
("{name}", 0, "{name}"),
("{name}", level1, "{name}{sep}{level}")
]
for _name, level, _expected_result in test_cases:
name = _name.format(name=name1, level=level1, sep=RECURRENTSTACK_SEPARATOR)
expected_result = _expected_result.format(name=name1, level=level1, sep=RECURRENTSTACK_SEPARATOR)
resut = RecurrentStack.suffix(name, level)
assert resut == expected_result, "expected suffix(\"{}\",{}) -> \"{}\" got \"{}\"".format(name, level,
expected_result,
resut)
def main(name, epochs, batch_size, learning_rate,
dim, mix_dim, old_model_name, max_length, bokeh, GRU, dropout,
depth, max_grad, step_method, epsilon, sample, skip, uniform, top):
#----------------------------------------------------------------------
datasource = name
def shnum(x):
""" Convert a positive float into a short tag-usable string
E.g.: 0 -> 0, 0.005 -> 53, 100 -> 1-2
"""
return '0' if x <= 0 else '%s%d' % (("%e"%x)[0], -np.floor(np.log10(x)))
jobname = "%s-%dX%dm%dd%dr%sb%de%s" % (datasource, depth, dim, mix_dim,
int(dropout*10),
shnum(learning_rate), batch_size,
shnum(epsilon))
if max_length != 600:
jobname += '-L%d'%max_length
if GRU:
jobname += 'g'
if max_grad != 5.:
jobname += 'G%g'%max_grad
if step_method != 'adam':
jobname += step_method
if skip:
jobname += 'D'
assert depth > 1
if top:
jobname += 'T'
assert depth > 1
if uniform > 0.:
jobname += 'u%d'%int(uniform*100)
if debug:
jobname += ".debug"
if sample:
print("Sampling")
else:
print("\nRunning experiment %s" % jobname)
if old_model_name:
print("starting from model %s"%old_model_name)
#----------------------------------------------------------------------
transitions = [GatedRecurrent(dim=dim) if GRU else LSTM(dim=dim)
for _ in range(depth)]
if depth > 1:
transition = RecurrentStack(transitions, name="transition",
skip_connections=skip or top)
if skip:
source_names=[RecurrentStack.suffix('states', d) for d in range(depth)]
else:
source_names=[RecurrentStack.suffix('states', depth-1)]
else:
transition = transitions[0]
transition.name = "transition"
source_names=['states']
emitter = SketchEmitter(mix_dim=mix_dim,
epsilon=epsilon,
name="emitter")
readout = Readout(
readout_dim=emitter.get_dim('inputs'),
source_names=source_names,
emitter=emitter,
name="readout")
generator = SequenceGenerator(readout=readout, transition=transition)
# Initialization settings
if uniform > 0.:
generator.weights_init = Uniform(width=uniform*2.)
else:
generator.weights_init = OrthogonalGlorot()
generator.biases_init = Constant(0)
# Build the cost computation graph [steps, batch_size, 3]
x = T.tensor3('features', dtype=floatX)
if debug:
x.tag.test_value = np.ones((max_length,batch_size,3)).astype(floatX)
x = x[:max_length,:,:] # has to be after setting test_value
cost = generator.cost(x)
cost.name = "sequence_log_likelihood"
# Give an idea of what's going on
model = Model(cost)
params = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in params.items()],
width=120))
model_size = 0
for v in params.itervalues():
s = v.get_value().shape
model_size += s[0] * (s[1] if len(s) > 1 else 1)
logger.info("Total number of parameters %d"%model_size)
#------------------------------------------------------------
extensions = []
if old_model_name:
if old_model_name == 'continue':
old_model_name = jobname
with open(old_model_name + '_model', "rb") as f:
old_model = pickle.load(f)
model.set_parameter_values(old_model.get_parameter_values())
del old_model
else:
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
if sample:
assert old_model_name and old_model_name != 'continue'
Sample(generator, steps=max_length, path=old_model_name).do(None)
exit(0)
#------------------------------------------------------------
# Define the training algorithm.
cg = ComputationGraph(cost)
if dropout > 0.:
from blocks.roles import INPUT, OUTPUT
dropout_target = VariableFilter(roles=[OUTPUT],
bricks=transitions,
name_regex='states')(cg.variables)
print('# dropout %d' % len(dropout_target))
cg = apply_dropout(cg, dropout_target, dropout)
opt_cost = cg.outputs[0]
else:
opt_cost = cost
if step_method == 'adam':
step_rule = Adam(learning_rate)
elif step_method == 'rmsprop':
step_rule = RMSProp(learning_rate, decay_rate=0.95)
elif step_method == 'adagrad':
step_rule = AdaGrad(learning_rate)
elif step_method == 'adadelta':
step_rule = AdaDelta()
elif step_method == 'scale':
step_rule = Scale(learning_rate)
else:
raise Exception('Unknown sttep method %s'%step_method)
step_rule = CompositeRule([StepClipping(max_grad), step_rule])
algorithm = GradientDescent(
cost=opt_cost, parameters=cg.parameters,
step_rule=step_rule)
#------------------------------------------------------------
observables = [cost]
# Fetch variables useful for debugging
(energies,) = VariableFilter(
applications=[generator.readout.readout],
name_regex="output")(cg.variables)
min_energy = energies.min().copy(name="min_energy")
max_energy = energies.max().copy(name="max_energy")
observables += [min_energy, max_energy]
# (activations,) = VariableFilter(
# applications=[generator.transition.apply],
# name=generator.transition.apply.states[0])(cg.variables)
# mean_activation = named_copy(abs(activations).mean(),
# "mean_activation")
# observables.append(mean_activation)
observables += [algorithm.total_step_norm, algorithm.total_gradient_norm]
for name, param in params.items():
observables.append(param.norm(2).copy(
name=name + "_norm"))
observables.append(algorithm.gradients[param].norm(2).copy(
name=name + "_grad_norm"))
#------------------------------------------------------------
datasource_fname = os.path.join(fuel.config.data_path[0], datasource,
datasource+'.hdf5')
train_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_sets=['train'], sources=('features',),
load_in_memory=True)
train_stream = DataStream(train_ds,
iteration_scheme=ShuffledScheme(
train_ds.num_examples, batch_size))
test_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_sets=['test'], sources=('features',),
load_in_memory=True)
test_stream = DataStream(test_ds,
iteration_scheme=SequentialScheme(
test_ds.num_examples, batch_size))
train_stream = Mapping(train_stream, _transpose)
test_stream = Mapping(test_stream, _transpose)
def stream_stats(ds, label):
itr = ds.get_epoch_iterator(as_dict=True)
batch_count = 0
examples_count = 0
for batch in itr:
batch_count += 1
examples_count += batch['features'].shape[1]
print('%s #batch %d #examples %d' %
(label, batch_count, examples_count))
stream_stats(train_stream, 'train')
stream_stats(test_stream, 'test')
extensions += [Timing(every_n_batches=10),
TrainingDataMonitoring(
observables, prefix="train",
every_n_batches=10),
DataStreamMonitoring(
[cost], # without dropout
test_stream,
prefix="test",
on_resumption=True,
after_epoch=False, # by default this is True
every_n_batches=100),
# all monitored data is ready so print it...
# (next steps may take more time and we want to see the
# results as soon as possible so print as soon as you can)
Printing(every_n_batches=10),
# perform multiple dumps at different intervals
# so if one of them breaks (has nan) we can hopefully
# find a model from few batches ago in the other
Checkpoint(jobname,
before_training=False, after_epoch=True,
save_separately=['log', 'model']),
Sample(generator, steps=max_length,
path=jobname+'.test',
every_n_batches=100),
ProgressBar(),
FinishAfter(after_n_epochs=epochs)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition(["after_batch"], _is_nan),
]
if bokeh:
from blocks.extras.extensions.plot import Plot
extensions.append(Plot(
'sketch',
channels=[['cost']], every_n_batches=10))
# Construct the main loop and start training!
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions
)
main_loop.run()
def main(name, epochs, batch_size, learning_rate,
dim, mix_dim, old_model_name, max_length, bokeh, GRU, dropout,
depth, max_grad, step_method, epsilon, sample, skip, uniform, top):
#----------------------------------------------------------------------
datasource = name
def shnum(x):
""" Convert a positive float into a short tag-usable string
E.g.: 0 -> 0, 0.005 -> 53, 100 -> 1-2
"""
return '0' if x <= 0 else '%s%d' % (("%e"%x)[0], -np.floor(np.log10(x)))
jobname = "%s-%dX%dm%dd%dr%sb%de%s" % (datasource, depth, dim, mix_dim,
int(dropout*10),
shnum(learning_rate), batch_size,
shnum(epsilon))
if max_length != 600:
jobname += '-L%d'%max_length
if GRU:
jobname += 'g'
if max_grad != 5.:
jobname += 'G%g'%max_grad
if step_method != 'adam':
jobname += step_method
if skip:
jobname += 'D'
assert depth > 1
if top:
jobname += 'T'
assert depth > 1
if uniform > 0.:
jobname += 'u%d'%int(uniform*100)
if debug:
jobname += ".debug"
if sample:
print("Sampling")
else:
print("\nRunning experiment %s" % jobname)
if old_model_name:
print("starting from model %s"%old_model_name)
#----------------------------------------------------------------------
transitions = [GatedRecurrent(dim=dim) if GRU else LSTM(dim=dim)
for _ in range(depth)]
if depth > 1:
transition = RecurrentStack(transitions, name="transition",
skip_connections=skip or top)
if skip:
source_names=[RecurrentStack.suffix('states', d) for d in range(depth)]
else:
source_names=[RecurrentStack.suffix('states', depth-1)]
else:
transition = transitions[0]
transition.name = "transition"
source_names=['states']
emitter = SketchEmitter(mix_dim=mix_dim,
epsilon=epsilon,
name="emitter")
readout = Readout(
readout_dim=emitter.get_dim('inputs'),
source_names=source_names,
emitter=emitter,
name="readout")
generator = SequenceGenerator(readout=readout, transition=transition)
# Initialization settings
if uniform > 0.:
generator.weights_init = Uniform(width=uniform*2.)
else:
generator.weights_init = OrthogonalGlorot()
generator.biases_init = Constant(0)
# Build the cost computation graph [steps, batch_size, 3]
x = T.tensor3('features', dtype=floatX)
if debug:
x.tag.test_value = np.ones((max_length,batch_size,3)).astype(floatX)
x = x[:max_length,:,:] # has to be after setting test_value
cost = generator.cost(x)
cost.name = "sequence_log_likelihood"
# Give an idea of what's going on
model = Model(cost)
params = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in params.items()],
width=120))
model_size = 0
for v in params.itervalues():
s = v.get_value().shape
model_size += s[0] * (s[1] if len(s) > 1 else 1)
logger.info("Total number of parameters %d"%model_size)
#------------------------------------------------------------
extensions = []
if old_model_name:
if old_model_name == 'continue':
old_model_name = jobname
with open(old_model_name + '_model', "rb") as f:
old_model = pickle.load(f)
model.set_parameter_values(old_model.get_parameter_values())
del old_model
else:
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
if sample:
assert old_model_name and old_model_name != 'continue'
Sample(generator, steps=max_length, path=old_model_name).do(None)
exit(0)
#------------------------------------------------------------
# Define the training algorithm.
cg = ComputationGraph(cost)
if dropout > 0.:
from blocks.roles import INPUT, OUTPUT
dropout_target = VariableFilter(roles=[OUTPUT],
bricks=transitions,
name_regex='states')(cg.variables)
print('# dropout %d' % len(dropout_target))
cg = apply_dropout(cg, dropout_target, dropout)
opt_cost = cg.outputs[0]
else:
opt_cost = cost
if step_method == 'adam':
step_rule = Adam(learning_rate)
elif step_method == 'rmsprop':
step_rule = RMSProp(learning_rate, decay_rate=0.95)
elif step_method == 'adagrad':
step_rule = AdaGrad(learning_rate)
elif step_method == 'adadelta':
step_rule = AdaDelta()
elif step_method == 'scale':
step_rule = Scale(learning_rate)
else:
raise Exception('Unknown sttep method %s'%step_method)
step_rule = CompositeRule([StepClipping(max_grad), step_rule])
algorithm = GradientDescent(
cost=opt_cost, parameters=cg.parameters,
step_rule=step_rule)
#------------------------------------------------------------
observables = [cost]
# Fetch variables useful for debugging
(energies,) = VariableFilter(
applications=[generator.readout.readout],
name_regex="output")(cg.variables)
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
observables += [min_energy, max_energy]
# (activations,) = VariableFilter(
# applications=[generator.transition.apply],
# name=generator.transition.apply.states[0])(cg.variables)
# mean_activation = named_copy(abs(activations).mean(),
# "mean_activation")
# observables.append(mean_activation)
observables += [algorithm.total_step_norm, algorithm.total_gradient_norm]
for name, param in params.items():
observables.append(named_copy(
param.norm(2), name + "_norm"))
observables.append(named_copy(
algorithm.gradients[param].norm(2), name + "_grad_norm"))
#------------------------------------------------------------
datasource_fname = os.path.join(fuel.config.data_path, datasource,
datasource+'.hdf5')
train_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_sets=['train'], sources=('features',),
load_in_memory=True)
train_stream = DataStream(train_ds,
iteration_scheme=ShuffledScheme(
train_ds.num_examples, batch_size))
test_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_sets=['test'], sources=('features',),
load_in_memory=True)
test_stream = DataStream(test_ds,
iteration_scheme=SequentialScheme(
test_ds.num_examples, batch_size))
train_stream = Mapping(train_stream, _transpose)
test_stream = Mapping(test_stream, _transpose)
def stream_stats(ds, label):
itr = ds.get_epoch_iterator(as_dict=True)
batch_count = 0
examples_count = 0
for batch in itr:
batch_count += 1
examples_count += batch['features'].shape[1]
print('%s #batch %d #examples %d' %
(label, batch_count, examples_count))
stream_stats(train_stream, 'train')
stream_stats(test_stream, 'test')
extensions += [Timing(every_n_batches=10),
TrainingDataMonitoring(
observables, prefix="train",
every_n_batches=10),
DataStreamMonitoring(
[cost], # without dropout
test_stream,
prefix="test",
on_resumption=True,
after_epoch=False, # by default this is True
every_n_batches=100),
# all monitored data is ready so print it...
# (next steps may take more time and we want to see the
# results as soon as possible so print as soon as you can)
Printing(every_n_batches=10),
# perform multiple dumps at different intervals
# so if one of them breaks (has nan) we can hopefully
# find a model from few batches ago in the other
Checkpoint(jobname,
before_training=False, after_epoch=True,
save_separately=['log', 'model']),
Sample(generator, steps=max_length,
path=jobname+'.test',
every_n_batches=100),
ProgressBar(),
FinishAfter(after_n_epochs=epochs)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition(["after_batch"], _is_nan),
]
if bokeh:
from blocks.extras.extensions.plot import Plot
extensions.append(Plot(
'sketch',
channels=[['cost']], every_n_batches=10))
# Construct the main loop and start training!
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions
)
main_loop.run()