def infer_onestep(self, feed_dict):
"""inference the gru-unit by one step"""
return executor_run_with_fetch_dict(self.base_exe,
program=self.infer_onestep_program,
fetch_dict=self.infer_onestep_outputs['fetch_dict'],
feed=feed_dict,
return_numpy=False,
scope=self.scope)
def infer_init(self, feed_dict):
"""inference only the init part"""
return executor_run_with_fetch_dict(self.base_exe,
program=self.infer_init_program,
fetch_dict=self.infer_init_outputs['fetch_dict'],
feed=feed_dict,
return_numpy=False,
scope=self.scope)
def softmax_sampling(self, feed_dict):
"""sampling"""
return executor_run_with_fetch_dict(self.base_exe,
program=self.softmax_sampling_program,
fetch_dict=self.softmax_sampling_outputs['fetch_dict'],
feed=feed_dict,
return_numpy=False,
scope=self.scope)
def test(self, list_feed_dict):
"""test"""
if self.mode == 'single':
assert len(list_feed_dict) == 1
return executor_run_with_fetch_dict(self.test_exe,
program=self.test_program,
fetch_dict=self.test_outputs['fetch_dict'],
feed=list_feed_dict[0],
return_numpy=False,
scope=self.scope)
elif self.mode == 'parallel' or self.mode == 'pserver':
return parallel_executor_run_with_fetch_dict(self.test_exe,
fetch_dict=self.test_outputs['fetch_dict'],
feed=list_feed_dict,
return_numpy=False)
def train(self, list_feed_dict):
"""train"""
with fluid.scope_guard(self.scope):
self.alg.before_every_batch()
if self.mode == 'single':
fetch_dict = executor_run_with_fetch_dict(
self.train_exe,
program=self.train_program,
fetch_dict=self.train_outputs['fetch_dict'],
feed=list_feed_dict,
return_numpy=False,
scope=self.scope)
elif self.mode == 'parallel' or self.mode == 'pserver':
fetch_dict = parallel_executor_run_with_fetch_dict(
self.train_exe,
fetch_dict=self.train_outputs['fetch_dict'],
feed=list_feed_dict,
return_numpy=False)
with fluid.scope_guard(self.scope):
self.alg.after_every_batch()
return fetch_dict
