def main(self):
print_mem('start')
#if test_data is not None:
self.test()
print 'Took', (time.time() - self.start_time)/60., 'min'
avg_step = timings['time_step'][:self.step].mean()
avg_cost2expl = timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(1 / avg_step * 86400 * state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
print_mem('start')
#if test_data is not None:
self.test()
print 'Took', (time.time() - self.start_time) / 60., 'min'
avg_step = timings['time_step'][:self.step].mean()
avg_cost2expl = timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(
1 / avg_step * 86400 * state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def validate(self):
rvals = self.model.validate(self.valid_data)
msg = '** %d validation:' % self.valid_id
print_mem('validate')
self.valid_id += 1
self.batch_start_time = time.time()
pos = self.step // self.state['validFreq']
for k, v in rvals:
msg = msg + ' ' + k + ':%f ' % float(v)
self.timings['valid'+k][pos] = float(v)
self.state['valid'+k] = float(v)
msg += 'whole time %s' % print_time(time.time() - self.start_time)
msg += ' patience %d' % self.patience
print msg
if self.train_cost:
valid_rvals = rvals
rvals = self.model.validate(self.train_data, True)
msg = '** %d train:' % (self.valid_id - 1)
for k, v in rvals:
msg = msg + ' ' + k + ':%6.3f ' % float(v)
self.timings['fulltrain' + k] = float(v)
self.state['fulltrain' + k] = float(v)
print msg
rvals = valid_rvals
self.state['validtime'] = float(time.time() - self.start_time)/60.
# Just pick the first thing that the cost returns
cost = rvals[0][1]
if self.state['bvalidcost'] > cost:
self.state['bvalidcost'] = float(cost)
for k, v in rvals:
self.state['bvalid'+k] = float(v)
self.state['bstep'] = int(self.step)
self.state['btime'] = int(time.time() - self.start_time)
self.test()
elif numpy.random.rand(1) > self.state['rand_test_inclusion']:
print 'Shouldn''t test, but you got lucky', cost, '>', self.state['bvalidcost']
for k, v in self.state.items():
if 'test' in k:
print k, v
self.test()
else:
print 'No testing', cost, '>', self.state['bvalidcost']
for k, v in self.state.items():
if 'test' in k:
print k, v
print_mem('validate')
if self.validate_postprocess:
return self.validate_postprocess(cost)
return cost
def validate(self):
rvals = self.model.validate(self.valid_data)
msg = "** %d validation:" % self.valid_id
self.valid_id += 1
self.batch_start_time = time.time()
pos = self.step // self.state["validFreq"]
for k, v in rvals:
msg = msg + " " + k + ":%f " % float(v)
self.timings["valid" + k][pos] = float(v)
self.state["valid" + k] = float(v)
msg += "whole time %s" % print_time(time.time() - self.start_time)
msg += " patience %d" % self.patience
print msg
if self.train_cost:
valid_rvals = rvals
rvals = self.model.validate(self.train_data, True)
msg = "** %d train:" % (self.valid_id - 1)
for k, v in rvals:
msg = msg + " " + k + ":%6.3f " % float(v)
self.timings["fulltrain" + k] = float(v)
self.state["fulltrain" + k] = float(v)
print msg
rvals = valid_rvals
self.state["validtime"] = float(time.time() - self.start_time) / 60.0
# Just pick the first thing that the cost returns
cost = rvals[0][1]
if self.state["bvalidcost"] > cost:
self.state["bvalidcost"] = float(cost)
for k, v in rvals:
self.state["bvalid" + k] = float(v)
self.state["bstep"] = int(self.step)
self.state["btime"] = int(time.time() - self.start_time)
self.test()
else:
print "No testing", cost, ">", self.state["bvalidcost"]
for k, v in self.state.items():
if "test" in k:
print k, v
print_mem("validate")
if self.validate_postprocess:
return self.validate_postprocess(cost)
return cost
def validate(self):
rvals = self.model.validate(self.valid_data)
msg = '** %d validation:' % self.valid_id
self.valid_id += 1
self.batch_start_time = time.time()
pos = self.step // self.state['validFreq']
for k, v in rvals:
msg = msg + ' ' + k + ':%f ' % float(v)
self.timings['valid' + k][pos] = float(v)
self.state['valid' + k] = float(v)
msg += 'whole time %s' % print_time(time.time() - self.start_time)
msg += ' patience %d' % self.patience
print msg
if self.train_cost:
valid_rvals = rvals
rvals = self.model.validate(self.train_data, True)
msg = '** %d train:' % (self.valid_id - 1)
for k, v in rvals:
msg = msg + ' ' + k + ':%6.3f ' % float(v)
self.timings['fulltrain' + k] = float(v)
self.state['fulltrain' + k] = float(v)
print msg
rvals = valid_rvals
self.state['validtime'] = float(time.time() - self.start_time) / 60.
# Just pick the first thing that the cost returns
cost = rvals[0][1]
if self.state['bvalidcost'] > cost:
self.state['bvalidcost'] = float(cost)
for k, v in rvals:
self.state['bvalid' + k] = float(v)
self.state['bstep'] = int(self.step)
self.state['btime'] = int(time.time() - self.start_time)
self.test()
else:
print 'No testing', cost, '>', self.state['bvalidcost']
for k, v in self.state.items():
if 'test' in k:
print k, v
print_mem('validate')
if self.validate_postprocess:
return self.validate_postprocess(cost)
return cost
def main(self):
assert self.reset == -1
print_mem('start')
self.state['gotNaN'] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
self.state['clr'] = self.state['lr']
self.train_data.start(self.timings['next_offset']
if 'next_offset' in self.timings
else -1)
while (self.step < self.state['loopIters'] and
last_cost > .1*self.state['minerr'] and
(time.time() - start_time)/60. < self.state['timeStop'] and
self.state['lr'] > self.state['minlr']):
print 'step:', self.step
if self.step > 0 and (time.time() - self.save_time)/60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
if self.state['save_by_iter'] and self.step % self.state['saveiter'] == 0:
self.save_DIY()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
self.timings[name][self.step] = float(numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] =\
numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
self.timings['step'] = self.step
self.timings['next_offset'] = self.train_data.next_offset
except KeyboardInterrupt:
break
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time)/60., 'min'
avg_step = self.timings['time_step'][:self.step].mean()
avg_cost2expl = self.timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(1 / avg_step * 86400 * self.state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
assert self.reset == -1
print_mem("start")
self.state["gotNaN"] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings["step"])
self.algo.step = self.step
if self.state["save_iter"] < 0:
self.save_iter = 0
self.state["save_iter"] = 0
self.save()
if self.channel is not None:
self.channel.save()
else: # Fake saving
self.save_iter += 1
self.state["save_iter"] = self.save_iter
self.save_time = time.time()
last_cost = 1.0
self.state["clr"] = self.state["lr"]
self.train_data.start(self.timings["next_offset"] if "next_offset" in self.timings else -1)
if self.state["rolling_vocab"]:
for i in xrange(self.timings["step"] - self.timings["super_step"]):
self.train_data.next()
if self.state["rolling_vocab"]:
# Make sure dictionary is current.
# If training is interrupted when the vocabularies are exchanged,
# things may get broken.
step_modulo = self.step % self.model.total_num_batches
if step_modulo in self.model.rolling_vocab_dict: # 0 always in.
cur_key = step_modulo
else:
cur_key = 0
for key in self.model.rolling_vocab_dict:
if (key < step_modulo) and (key > cur_key): # Find largest key smaller than step_modulo
cur_key = key
new_large2small_src = self.model.Dx_shelve[str(cur_key)]
new_large2small_trgt = self.model.Dy_shelve[str(cur_key)]
self.roll_vocab_update_dicts(new_large2small_src, new_large2small_trgt)
self.zero_or_reload = True
while (
self.step < self.state["loopIters"]
and last_cost > 0.1 * self.state["minerr"]
and (time.time() - start_time) / 60.0 < self.state["timeStop"]
and self.state["lr"] > self.state["minlr"]
):
if self.step > 0 and (time.time() - self.save_time) / 60.0 >= self.state["saveFreq"]:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
if self.state["rolling_vocab"]:
step_modulo = self.step % self.model.total_num_batches
if step_modulo in self.model.rolling_vocab_dict:
if not self.zero_or_reload:
self.roll_vocab_small2large() # Not necessary for 0 or when reloading a properly saved model
new_large2small_src = self.model.Dx_shelve[str(step_modulo)]
new_large2small_trgt = self.model.Dy_shelve[str(step_modulo)]
self.roll_vocab_update_dicts(
new_large2small_src, new_large2small_trgt
) # Done above for 0 or reloaded model
self.roll_vocab_large2small()
try:
tmp_batch = self.train_data.next(peek=True)
except StopIteration:
if self.state["reprocess_each_iteration"]:
logger.info("Reached end of file; re-preprocessing")
subprocess.check_call(self.state["reprocess_each_iteration"], shell=True)
if self.state["rolling_vocab"]:
os.remove(self.state["Dx_file"])
os.remove(self.state["Dy_file"])
tmp_state = copy.deepcopy(self.state)
rolling_dicts.main(tmp_state)
with open(self.state["rolling_vocab_dict"], "rb") as f:
self.model.rolling_vocab_dict = cPickle.load(f)
self.model.total_num_batches = max(self.model.rolling_vocab_dict)
self.model.Dx_shelve = shelve.open(self.state["Dx_file"])
self.model.Dy_shelve = shelve.open(self.state["Dy_file"])
# round up/down number of steps so modulo is 0 (hack because total_num_batches can change)
logger.debug("step before restart: {0}".format(self.step))
if self.step % self.model.total_num_batches < self.model.total_num_batches / 2:
self.step -= self.step % self.model.total_num_batches
else:
self.step += self.model.total_num_batches - (
self.step % self.model.total_num_batches
)
logger.debug("step after restart: {0}".format(self.step))
logger.debug("Load data")
self.train_data = get_batch_iterator(
self.state, numpy.random.RandomState(self.state["seed"])
)
self.train_data.start(-1)
self.timings["next_offset"] = -1
step_modulo = self.step % self.model.total_num_batches
if step_modulo in self.model.rolling_vocab_dict:
if not self.zero_or_reload:
self.roll_vocab_small2large() # Not necessary for 0 or when reloading a properly saved model
new_large2small_src = self.model.Dx_shelve[str(step_modulo)]
new_large2small_trgt = self.model.Dy_shelve[str(step_modulo)]
self.roll_vocab_update_dicts(
new_large2small_src, new_large2small_trgt
) # Done above for 0 or reloaded model
self.roll_vocab_large2small()
self.algo.data = self.train_data
self.algo.step = self.step
tmp_batch = self.train_data.next(peek=True)
if self.hooks:
self.hooks[0].train_iter = self.train_data
else:
self.save()
raise
if (
tmp_batch["x"][:, 0].tolist(),
tmp_batch["y"][:, 0].tolist(),
) == self.model.rolling_vocab_dict[step_modulo]:
logger.debug("Identical first sentences. OK")
else:
logger.error("Batches do not correspond.")
elif self.state["hookFreq"] > 0 and self.step % self.state["hookFreq"] == 0 and self.hooks:
[fn() for fn in self.hooks]
# Hook first so that the peeked batch is the same as the one used in algo
# Use elif not to peek twice
try:
rvals = self.algo()
except StopIteration:
if self.state["reprocess_each_iteration"]:
logger.info("Reached end of file; re-preprocessing")
subprocess.check_call(self.state["reprocess_each_iteration"], shell=True)
logger.debug("Load data")
self.train_data = get_batch_iterator(self.state, numpy.random.RandomState(self.state["seed"]))
self.train_data.start(-1)
self.timings["next_offset"] = -1
self.algo.data = self.train_data
self.algo.step = self.step
rvals = self.algo()
if self.hooks:
self.hooks[0].train_iter = self.train_data
else:
self.save()
raise
self.state["traincost"] = float(rvals["cost"])
self.state["step"] = self.step
last_cost = rvals["cost"]
for name in rvals.keys():
self.timings[name][self.step] = float(numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] = numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals["cost"]) or numpy.isnan(rvals["cost"])) and self.state["on_nan"] == "raise":
self.state["gotNaN"] = 1
self.save()
if self.channel:
self.channel.save()
print "Got NaN while training"
last_cost = 0
if self.valid_data is not None and self.step % self.state["validFreq"] == 0 and self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state["cost_threshold"]:
self.patience -= 1
if "lr_start" in self.state and self.state["lr_start"] == "on_error":
self.state["lr_start"] = self.step
elif valcost < self.old_cost:
self.patience = self.state["patience"]
self.old_cost = valcost
if self.state["divide_lr"] and self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state["divide_lr"]
bparams = dict(self.model.best_params)
self.patience = self.state["patience"]
for p in self.model.params:
p.set_value(bparams[p.name])
if not self.state["rolling_vocab"]: # Standard use of hooks
if self.state["hookFreq"] > 0 and self.step % self.state["hookFreq"] == 0 and self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and self.step % self.reset == 0:
print "Resetting the data iterator"
self.train_data.reset()
self.step += 1
if self.state["rolling_vocab"]:
self.zero_or_reload = False
self.timings["step"] = self.step # Step now
if (self.step % self.model.total_num_batches) % self.state[
"sort_k_batches"
] == 0: # Start of a super_batch.
logger.debug("Set super_step and next_offset")
# This log shoud appear just before 'logger.debug("Start of a super batch")' in 'get_homogeneous_batch_iter()'
self.timings["super_step"] = self.step
# Step at start of superbatch. super_step < step
self.timings["next_offset"] = self.train_data.next_offset
# Where to start after reload. Will need to call next() a few times
else:
self.timings["step"] = self.step
self.timings["next_offset"] = self.train_data.next_offset
except KeyboardInterrupt:
break
if self.state["rolling_vocab"]:
self.roll_vocab_small2large()
self.state["wholetime"] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print "Took", (time.time() - start_time) / 60.0, "min"
avg_step = self.timings["time_step"][: self.step].mean()
avg_cost2expl = self.timings["log2_p_expl"][: self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(1 / avg_step * 86400 * self.state["bs"])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
assert self.reset == -1
print_mem('start')
self.state['gotNaN'] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
print 'syscomb'
last_cost = 1.
self.state['clr'] = self.state['lr']
self.train_data.start(self.timings['next_offset'] if 'next_offset' in
self.timings else -1)
while (self.step < self.state['loopIters']
and last_cost > .1 * self.state['minerr']
and (time.time() - start_time) / 60. < self.state['timeStop']
and self.state['lr'] > self.state['minlr']):
print 'step:', self.step
if self.step > 0 and (time.time() - self.save_time
) / 60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
if self.state['save_by_iter'] and self.step % self.state[
'saveiter'] == 0:
self.save_DIY()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
self.timings[name][self.step] = float(
numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] =\
numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
try:
[fn() for fn in self.hooks]
except:
print 'sample failed'
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
self.timings['step'] = self.step
self.timings['next_offset'] = self.train_data.next_offset
except KeyboardInterrupt:
break
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time) / 60., 'min'
avg_step = self.timings['time_step'][:self.step].mean()
avg_cost2expl = self.timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(
1 / avg_step * 86400 * self.state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
assert self.reset == -1
print_mem('start')
self.state['gotNaN'] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
self.state['clr'] = self.state['lr']
self.train_data.start(self.timings['next_offset']
if 'next_offset' in self.timings
else -1)
# added by Zhaopeng Tu, 2016-01-08
# for halving the learning rate every full epoch after the 10th epoch
# in our experiences, the best performance is achieved around 10th epoch, then decreases after that
# that's why we introduce the strategy of havling the learning rate
epoch_batch_number = int(self.train_data.data_len / self.state['bs'])
print 'Iterations per epoch', epoch_batch_number
while (self.step < self.state['loopIters'] and
last_cost > .1*self.state['minerr'] and
(time.time() - start_time)/60. < self.state['timeStop'] and
self.state['lr'] > self.state['minlr']):
if self.step > 0 and (time.time() - self.save_time)/60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
# print 'Doubly cost weight:', self.model.cost_layer.DC.get_value(), 'ori_cost:', self.model.cost_layer.ori_cost.get_value(), 'doubly_cost:', self.model.cost_layer.doubly_cost.get_value(), 'cost:', self.model.cost_layer.cost.get_value()
# print 'Doubly cost weight:', self.model.cost_layer.DC.get_value()
# print_mem('iter%d'%self.step)
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
self.timings[name][self.step] = float(numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] =\
numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
self.timings['step'] = self.step
self.timings['next_offset'] = self.train_data.next_offset
# added by Zhaopeng Tu, 2016-01-08
# for halving the learning rate every full epoch after the 10th epoch
# if self.step % self.train_data.data_len == 0 and self.step / self.train_data.data_len >= 10:
if self.step % epoch_batch_number == 0 and self.step / epoch_batch_number >= 10:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
except KeyboardInterrupt:
break
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time)/60., 'min'
avg_step = self.timings['time_step'][:self.step].mean()
avg_cost2expl = self.timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(1 / avg_step * 86400 * self.state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
assert self.reset == -1
print_mem('start')
self.state['gotNaN'] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
if self.state['save_iter'] < 0:
self.save_iter = 0
self.state['save_iter'] = 0
self.save()
if self.channel is not None:
self.channel.save()
else: # Fake saving
self.save_iter += 1
self.state['save_iter'] = self.save_iter
self.save_time = time.time()
last_cost = 1.
self.state['clr'] = self.state['lr']
self.train_data.start(self.timings['next_offset']
if 'next_offset' in self.timings
else -1)
if self.state['rolling_vocab']:
for i in xrange(self.timings['step'] - self.timings['super_step']):
self.train_data.next()
if self.state['rolling_vocab']:
# Make sure dictionary is current.
# If training is interrupted when the vocabularies are exchanged,
# things may get broken.
step_modulo = self.step % self.model.total_num_batches
if step_modulo in self.model.rolling_vocab_dict: # 0 always in.
cur_key = step_modulo
else:
cur_key = 0
for key in self.model.rolling_vocab_dict:
if (key < step_modulo) and (key > cur_key): # Find largest key smaller than step_modulo
cur_key = key
new_large2small_src = self.model.Dx_shelve[str(cur_key)]
new_large2small_trgt = self.model.Dy_shelve[str(cur_key)]
self.roll_vocab_update_dicts(new_large2small_src, new_large2small_trgt)
self.zero_or_reload = True
while (self.step < self.state['loopIters'] and
last_cost > .1*self.state['minerr'] and
(time.time() - start_time)/60. < self.state['timeStop'] and
self.state['lr'] > self.state['minlr']):
if self.step > 0 and (time.time() - self.save_time)/60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
if self.state['rolling_vocab']:
step_modulo = self.step % self.model.total_num_batches
if step_modulo in self.model.rolling_vocab_dict:
if not self.zero_or_reload:
self.roll_vocab_small2large() # Not necessary for 0 or when reloading a properly saved model
new_large2small_src = self.model.Dx_shelve[str(step_modulo)]
new_large2small_trgt = self.model.Dy_shelve[str(step_modulo)]
self.roll_vocab_update_dicts(new_large2small_src, new_large2small_trgt) # Done above for 0 or reloaded model
self.roll_vocab_large2small()
tmp_batch = self.train_data.next(peek=True)
if (tmp_batch['x'][:,0].tolist(), tmp_batch['y'][:,0].tolist()) == self.model.rolling_vocab_dict[step_modulo]:
logger.debug("Identical first sentences. OK")
else:
logger.error("Batches do not correspond.")
elif self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
# Hook first so that the peeked batch is the same as the one used in algo
# Use elif not to peek twice
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
self.timings[name][self.step] = float(numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] =\
numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if not self.state['rolling_vocab']: # Standard use of hooks
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
if self.state['rolling_vocab']:
self.zero_or_reload = False
self.timings['step'] = self.step # Step now
if (self.step % self.model.total_num_batches) % self.state['sort_k_batches'] == 0: # Start of a super_batch.
logger.debug("Set super_step and next_offset")
# This log shoud appear just before 'logger.debug("Start of a super batch")' in 'get_homogeneous_batch_iter()'
self.timings['super_step'] = self.step
# Step at start of superbatch. super_step < step
self.timings['next_offset'] = self.train_data.next_offset
# Where to start after reload. Will need to call next() a few times
else:
self.timings['step'] = self.step
self.timings['next_offset'] = self.train_data.next_offset
except KeyboardInterrupt:
break
if self.state['rolling_vocab']:
self.roll_vocab_small2large()
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time)/60., 'min'
avg_step = self.timings['time_step'][:self.step].mean()
avg_cost2expl = self.timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(1 / avg_step * 86400 * self.state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
print_mem('start')
self.state['gotNaN'] = 0
self.start_time = time.time()
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
start_time = time.time()
self.start_time = start_time
self.state['clr'] = self.state['lr']
while (self.step < self.state['loopIters']
and last_cost > .1 * self.state['minerr']
and (time.time() - start_time) / 60. < self.state['timeStop']
and self.state['lr'] > self.state['minlr']):
if (time.time() - self.save_time) / 60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
pos = self.step // self.state['trainFreq']
self.timings[name][pos] = float(numpy.array(rvals[name]))
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
self.timings['step'] = self.step
print "took {}".format(time.time() - st)
except:
self.state['wholetime'] = float(time.time() - start_time)
self.save()
if self.channel:
self.channel.save()
last_cost = 0
print 'Error in running algo (lr issue)'
print 'Took', (time.time() - start_time) / 60., 'min'
raise
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time) / 60., 'min'
def main(self):
print_mem('start')
self.state['gotNaN'] = 0
self.start_time = time.time()
self.batch_start_time = time.time()
self.step = 0
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
start_time = time.time()
self.start_time = start_time
self.state['clr'] = self.state['lr']
while (self.step < self.state['loopIters'] and
last_cost > .1*self.state['minerr'] and
(time.time() - start_time)/60. < self.state['timeStop'] and
self.state['lr'] > self.state['minlr']):
if (time.time() - self.save_time)/60. > self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
pos = self.step // self.state['trainFreq']
self.timings[name][pos] = float(numpy.array(rvals[name]))
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
except:
self.state['wholetime'] = float(time.time() - start_time)
self.save()
if self.channel:
self.channel.save()
last_cost = 0
print 'Error in running algo (lr issue)'
print 'Took', (time.time() - start_time)/60., 'min'
raise
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time)/60., 'min'
def main(self):
assert self.reset == -1
print_mem('start')
self.state['gotNaN'] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
self.state['clr'] = self.state['lr']
self.train_data.start(self.timings['next_offset'] if 'next_offset' in
self.timings else -1)
# added by Zhaopeng Tu, 2016-01-08
# for halving the learning rate every full epoch after the 10th epoch
# in our experiences, the best performance is achieved around 10th epoch, then decreases after that
# that's why we introduce the strategy of havling the learning rate
epoch_batch_number = int(self.train_data.data_len / self.state['bs'])
print 'Iterations per epoch', epoch_batch_number
while (self.step < self.state['loopIters']
and last_cost > .1 * self.state['minerr']
and (time.time() - start_time) / 60. < self.state['timeStop']
and self.state['lr'] > self.state['minlr']):
if self.step > 0 and (time.time() - self.save_time
) / 60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
# print 'Doubly cost weight:', self.model.cost_layer.DC.get_value(), 'ori_cost:', self.model.cost_layer.ori_cost.get_value(), 'doubly_cost:', self.model.cost_layer.doubly_cost.get_value(), 'cost:', self.model.cost_layer.cost.get_value()
# print 'Doubly cost weight:', self.model.cost_layer.DC.get_value()
# print_mem('iter%d'%self.step)
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
self.timings[name][self.step] = float(
numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] =\
numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
self.timings['step'] = self.step
self.timings['next_offset'] = self.train_data.next_offset
# added by Zhaopeng Tu, 2016-01-08
# for halving the learning rate every full epoch after the 10th epoch
# if self.step % self.train_data.data_len == 0 and self.step / self.train_data.data_len >= 10:
if self.step % epoch_batch_number == 0 and self.step / epoch_batch_number >= 10:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
except KeyboardInterrupt:
break
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time) / 60., 'min'
avg_step = self.timings['time_step'][:self.step].mean()
avg_cost2expl = self.timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(
1 / avg_step * 86400 * self.state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
assert self.reset == -1
print_mem('start')
self.state['gotNaN'] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
self.state['clr'] = self.state['lr']
###############################################################################
# by He Wei
if 'next_offset' in self.state and self.state['next_offset'] <= 0:
self.train_data.start(-1)
elif 'next_offset' in self.timings:
self.train_data.start(self.timings['next_offset'])
else:
self.train_data.start(-1)
#self.train_data.start(self.timings['next_offset']
# if 'next_offset' in self.timings
# else -1)
#if 'next_offset' in self.state and self.state['next_offset'] <= 0:
# self.train_data.reset()
while (self.step < self.state['loopIters'] and
last_cost > .1*self.state['minerr'] and
(time.time() - start_time)/60. < self.state['timeStop'] and
self.state['lr'] > self.state['minlr']):
####################################################################################
#add by hewei
if self.step % int(self.state['copy_model_freq']) == 0:
self.save()
if self.channel is not None:
self.channel.save()
try:
copy_model_path = "%s/iter_%d" % (self.state['copy_model_path'], self.step)
cmd = "mkdir -p %s" % copy_model_path
if not os.path.exists(copy_model_path):
print >> sys.stderr, cmd
os.system(cmd)
cmd = "cp %s* %s" % (self.state['prefix'], copy_model_path)
print >> sys.stderr, cmd
os.system(cmd)
except Exception:
print 'mainLoop: failed to copy model file to %s' % copy_model_path
traceback.print_exc()
####################################################################################
if self.step > 0 and (time.time() - self.save_time)/60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
self.timings[name][self.step] = float(numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] =\
numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
self.timings['step'] = self.step
self.timings['next_offset'] = self.train_data.next_offset
except KeyboardInterrupt:
break
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time)/60., 'min'
avg_step = self.timings['time_step'][:self.step].mean()
avg_cost2expl = self.timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(1 / avg_step * 86400 * self.state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
def main(self):
assert self.reset == -1
print_mem('start')
self.state['gotNaN'] = 0
start_time = time.time()
self.start_time = start_time
self.batch_start_time = time.time()
self.step = int(self.timings['step'])
self.algo.step = self.step
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
self.state['clr'] = self.state['lr']
###############################################################################
# by He Wei
if 'next_offset' in self.state and self.state['next_offset'] <= 0:
self.train_data.start(-1)
elif 'next_offset' in self.timings:
self.train_data.start(self.timings['next_offset'])
else:
self.train_data.start(-1)
#self.train_data.start(self.timings['next_offset']
# if 'next_offset' in self.timings
# else -1)
#if 'next_offset' in self.state and self.state['next_offset'] <= 0:
# self.train_data.reset()
while (self.step < self.state['loopIters']
and last_cost > .1 * self.state['minerr']
and (time.time() - start_time) / 60. < self.state['timeStop']
and self.state['lr'] > self.state['minlr']):
####################################################################################
#add by hewei
if self.step % int(self.state['copy_model_freq']) == 0:
self.save()
if self.channel is not None:
self.channel.save()
try:
copy_model_path = "%s/iter_%d" % (
self.state['copy_model_path'], self.step)
cmd = "mkdir -p %s" % copy_model_path
if not os.path.exists(copy_model_path):
print >> sys.stderr, cmd
os.system(cmd)
cmd = "cp %s* %s" % (self.state['prefix'], copy_model_path)
print >> sys.stderr, cmd
os.system(cmd)
except Exception:
print 'mainLoop: failed to copy model file to %s' % copy_model_path
traceback.print_exc()
####################################################################################
if self.step > 0 and (time.time() - self.save_time
) / 60. >= self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
self.timings[name][self.step] = float(
numpy.array(rvals[name]))
if self.l2_params:
for param in self.model.params:
self.timings["l2_" + param.name][self.step] =\
numpy.mean(param.get_value() ** 2) ** 0.5
if (numpy.isinf(rvals['cost']) or
numpy.isnan(rvals['cost'])) and\
self.state['on_nan'] == 'raise':
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.valid_data is not None and\
self.step % self.state['validFreq'] == 0 and\
self.step > 1:
valcost = self.validate()
if valcost > self.old_cost * self.state['cost_threshold']:
self.patience -= 1
if 'lr_start' in self.state and\
self.state['lr_start'] == 'on_error':
self.state['lr_start'] = self.step
elif valcost < self.old_cost:
self.patience = self.state['patience']
self.old_cost = valcost
if self.state['divide_lr'] and \
self.patience < 1:
# Divide lr by 2
self.algo.lr = self.algo.lr / self.state['divide_lr']
bparams = dict(self.model.best_params)
self.patience = self.state['patience']
for p in self.model.params:
p.set_value(bparams[p.name])
if self.state['hookFreq'] > 0 and \
self.step % self.state['hookFreq'] == 0 and \
self.hooks:
[fn() for fn in self.hooks]
if self.reset > 0 and self.step > 1 and \
self.step % self.reset == 0:
print 'Resetting the data iterator'
self.train_data.reset()
self.step += 1
self.timings['step'] = self.step
self.timings['next_offset'] = self.train_data.next_offset
except KeyboardInterrupt:
break
self.state['wholetime'] = float(time.time() - start_time)
if self.valid_data is not None:
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'Took', (time.time() - start_time) / 60., 'min'
avg_step = self.timings['time_step'][:self.step].mean()
avg_cost2expl = self.timings['log2_p_expl'][:self.step].mean()
print "Average step took {}".format(avg_step)
print "That amounts to {} sentences in a day".format(
1 / avg_step * 86400 * self.state['bs'])
print "Average log2 per example is {}".format(avg_cost2expl)
