def __init__(self,
name,
data,
model,
optimizer,
cost,
outputs,
debug_print=0,
trainlog=None,
extension=None):
self.name = name
self.data = data
self.model = model
self.optimizer = optimizer
self.inputs = model.inputs
self.cost = cost
self.outputs = tolist(outputs)
self.updates = OrderedDict()
self.updates.update(model.updates)
self.extension = extension
self.debug_print = debug_print
t0 = time.time()
self.cost_fn = self.build_training_graph()
print "Elapsed compilation time: %f" % (time.time() - t0)
if self.debug_print:
from theano.printing import debugprint
debugprint(self.cost_fn)
if trainlog is None:
self.trainlog = TrainLog()
else:
self.trainlog = trainlog
self.endloop = 0
def build_training_graph(self):
self.run_extension('ext_regularize_pre_grad')
self.grads = OrderedDict(izip(self.params,
T.grad(self.cost, self.params)))
self.run_extension('ext_grad')
grads = self.optimizer.get_updates(self.grads)
for key, val in grads.items():
self.updates[key] = val
self.run_extension('ext_regularize_post_grad')
return self.build_theano_graph(self.inputs, self.outputs, self.updates)
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
output = FullyConnectedLayer(name='output',
parent=['h1', 'h2', 'h3'],
parent_dim=[200, 200, 200],
nout=frame_size,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [h1, h2, h3, output]
params = OrderedDict()
for node in nodes:
params.update(node.initialize())
params = init_tparams(params)
s1_0 = h1.get_init_state(batch_size)
s2_0 = h2.get_init_state(batch_size)
s3_0 = h3.get_init_state(batch_size)
def inner_fn(x_t, s1_tm1, s2_tm1, s3_tm1):
s1_t = h1.fprop([[x_t], [s1_tm1, s2_tm1, s3_tm1]], params)
s2_t = h2.fprop([[s1_t], [s2_tm1, s1_tm1, s3_tm1]], params)
s3_t = h3.fprop([[s2_t], [s3_tm1], s1_tm1, s2_tm1], params)
y_hat_t = output.fprop([s1_t, s2_t, s3_t], params)
class Training(PickleMixin, TheanoMixin):
"""
WRITEME
Parameters
----------
.. todo::
"""
def __init__(self,
name,
data,
model,
optimizer,
cost,
outputs,
debug_print=0,
trainlog=None,
extension=None):
self.name = name
self.data = data
self.model = model
self.optimizer = optimizer
self.inputs = model.inputs
self.cost = cost
self.outputs = tolist(outputs)
self.updates = OrderedDict()
self.updates.update(model.updates)
self.extension = extension
self.debug_print = debug_print
t0 = time.time()
self.cost_fn = self.build_training_graph()
print "Elapsed compilation time: %f" % (time.time() - t0)
if self.debug_print:
from theano.printing import debugprint
debugprint(self.cost_fn)
if trainlog is None:
self.trainlog = TrainLog()
else:
self.trainlog = trainlog
self.endloop = 0
def build_training_graph(self):
self.run_extension('ext_regularize_pre_grad')
self.grads = OrderedDict(izip(self.model.params.values(),
T.grad(self.cost, self.model.params.values())))
self.run_extension('ext_grad')
grads = self.optimizer.get_updates(self.grads)
for key, val in grads.items():
self.updates[key] = val
self.run_extension('ext_regularize_post_grad')
return self.build_theano_graph(self.inputs, self.outputs, self.updates)
def run(self):
logger.info("Entering main loop")
while self.run_epoch():
pass
logger.info("Terminating main loop")
def run_epoch(self):
for batch in self.data:
self.run_extension('ext_monitor')
batch_t0 = time.time()
this_cost = self.cost_fn(*batch)
self.trainlog._times.append(time.time() - batch_t0)
self.trainlog._batches.append(this_cost)
self.trainlog._batch_seen += 1
self.run_extension('ext_save')
self.trainlog._epoch_seen += 1
self.run_extension('ext_term')
if self.end_training():
self.run_extension('ext_monitor')
self.run_extension('ext_save')
return False
return True
def find_extension(self, name):
try:
exts = [extension for extension in self.extension
if extension.name == name]
if len(exts) > 0:
return_val = 1
else:
return_val = 0
return return_val, exts
except:
return (0, None)
def run_extension(self, name):
tok, exts = self.find_extension(name)
if tok:
for ext in exts:
ext.exe(self)
def end_training(self):
return self.endloop
