def train(self,
input_fn,
auc_fn=auc,
auc_interval=100,
auc_bucket_num=200,
max_step=sys.maxint,
checkpoint_interval=None,
log_format=LOG_FMT,
user_hooks=None):
'''
Args:
input_fn:
auc_fn:
auc_interval:
max_step:
checkpoint_interval:
log_format:
user_hooks:
'''
data, labels = input_fn()
model_outputs = self._model_fn(data, labels)
if len(model_outputs) < 2:
raise ArgumentError("model_fn must return loss and logits")
loss = model_outputs[0]
logits = model_outputs[1]
train_op = self._optimizer.optimize()
auc_op = auc_fn(logits, labels, num_thresholds=auc_bucket_num)
hooks = []
hooks.append(QpsMetricsHook())
hooks.append(MetricsHook("auc", auc_op, interval=auc_interval))
if user_hooks is not None:
if isinstance(user_hooks, list):
hooks.extend(user_hooks)
else:
hooks.append(user_hooks)
reader_hooks = get_collection(READER_HOOKS)
if reader_hooks is not None:
hooks.extend(reader_hooks)
if checkpoint_interval and get_task_index() == 0:
hooks.append(CheckpointHook(checkpoint_interval))
hooks.append(MetricsPrinterHook(log_format, auc_interval))
sess = TrainSession(hooks=hooks)
i = 0
while not sess.should_stop() and i < max_step:
sess.run(train_op)
i = i + 1
def evaluate(self,
input_fn,
checkpoint_version="",
log_format=EVAL_LOG_FMT,
log_interval=100,
max_step=sys.maxint,
auc_fn=auc,
auc_bucket_num=200,
user_hooks=None):
'''
Args:
input_fn:
checkpoint_version:
log_format:
log_interval:
max_step:
auc_fn:
user_hooks:
'''
from xdl.python.training.saver import Saver
if get_task_index() == 0:
saver = Saver()
saver.restore(checkpoint_version)
data, labels = input_fn()
model_outputs = self._model_fn(data, labels)
if len(model_outputs) < 2:
raise ArgumentError("model_fn must return loss and logits")
logits = model_outputs[1]
auc_op = auc_fn(logits, labels, num_thresholds=auc_bucket_num)
hooks = []
hooks.append(QpsMetricsHook())
hooks.append(MetricsHook("auc", auc_op, interval=1))
if user_hooks is not None:
if isinstance(user_hooks, list):
hooks.extend(user_hooks)
else:
hooks.append(user_hooks)
hooks.append(MetricsPrinterHook(log_format, log_interval))
sess = TrainSession(hooks=hooks)
if id(auc_fn) == id(auc):
sess.run(reset_auc_variables_op(auc_bucket_num))
i = 0
while not sess.should_stop() and i < max_step:
sess.run([])
i = i + 1
def predict(self,
input_fn,
checkpoint_version="",
log_format=PREDICT_LOG_FMT,
log_interval=100,
max_step=sys.maxint,
user_hooks=None):
'''
Args:
input_fn:
checkpoint_version:
log_format:
log_interval:
max_step:
user_hooks:
'''
from xdl.python.training.saver import Saver
if get_task_index() == 0:
saver = Saver()
saver.restore(checkpoint_version)
data, labels = input_fn()
model_outputs = self._model_fn(data, labels)
if len(model_outputs) < 2:
raise ArgumentError("model_fn must return loss and logits")
logits = model_outputs[1]
hooks = []
hooks.append(QpsMetricsHook())
hooks.append(MetricsHook("prediction", logits, interval=1))
if user_hooks is not None:
if isinstance(user_hooks, list):
hooks.extend(user_hooks)
else:
hooks.append(user_hooks)
hooks.append(MetricsPrinterHook(log_format, log_interval))
sess = TrainSession(hooks=hooks)
i = 0
while not sess.should_stop() and i < max_step:
sess.run([])
i = i + 1
def train_and_evaluate(self,
train_input_fn,
eval_input_fn,
eval_interval,
eval_steps,
checkpoint_interval,
auc_fn=auc,
auc_bucket_num=200,
train_hooks=None,
eval_hooks=None,
auc_interval=100,
log_interval=100,
log_format=LOG_FMT,
eval_log_format=EVAL_LOG_FMT,
max_step=sys.maxint):
with model_scope('train'):
datas, labels = train_input_fn()
train_outputs = self._model_fn(datas, labels)
if len(train_outputs) < 2:
raise ArgumentError("model_fn must return loss and logits")
loss = train_outputs[0]
logits = train_outputs[1]
train_op = self._optimizer.optimize()
auc_op = auc_fn(logits,
labels,
num_thresholds=auc_bucket_num,
namescope="train_auc")
train_hooks = []
train_hooks.append(QpsMetricsHook())
train_hooks.append(
MetricsHook("auc", auc_op, interval=auc_interval))
if train_hooks is not None:
if isinstance(train_hooks, list):
train_hooks.extend(train_hooks)
else:
train_hooks.append(train_hooks)
reader_hooks = get_collection(READER_HOOKS)
if reader_hooks is not None:
train_hooks.extend(reader_hooks)
if checkpoint_interval and get_task_index() == 0:
train_hooks.append(CheckpointHook(checkpoint_interval))
train_hooks.append(MetricsPrinterHook(log_format, auc_interval))
train_sess = TrainSession(hooks=train_hooks)
with model_scope('test'):
eval_datas, eval_labels = eval_input_fn()
eval_outputs = self._model_fn(eval_datas, eval_labels)
if len(eval_outputs) < 2:
raise ArgumentError("model_fn must return loss and logits")
eval_logits = eval_outputs[1]
eval_auc_op = auc_fn(eval_logits,
eval_labels,
num_thresholds=auc_bucket_num,
namescope="eval_auc")
eval_hooks = []
eval_hooks.append(QpsMetricsHook())
eval_hooks.append(MetricsHook("auc", eval_auc_op, interval=1))
if eval_hooks is not None:
if isinstance(eval_hooks, list):
eval_hooks.extend(eval_hooks)
else:
eval_hooks.append(eval_hooks)
eval_hooks.append(MetricsPrinterHook(eval_log_format,
log_interval))
eval_sess = TrainSession(hooks=eval_hooks)
lstep = 0
while True:
print('\n>>> start train at local step[%d]\n' % lstep)
while not train_sess.should_stop() and (lstep == 0 or lstep % eval_interval != 0) \
and lstep < max_step:
train_sess.run(train_op)
lstep = lstep + 1
lstep = lstep + 1
eval_step = 0
print('\n>>> start evaluate at local step[%d]\n' % lstep)
while not eval_sess.should_stop() and eval_step < eval_steps:
eval_sess.run([])
eval_step = eval_step + 1
if train_sess.should_stop() or lstep >= max_step:
break