def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in xrange(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost:" + str(cost))
if batch_id != 0:
print("second per batch: " +
str((time.time() - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(cost) < 60.0:
if save_dirname is not None:
# TODO(liuyiqun): Change the target to crf_decode
fluid.io.save_inference_model(
save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
return
batch_id = batch_id + 1
def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in xrange(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost:" + str(cost))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(cost) < 60.0:
if save_dirname is not None:
# TODO(liuyiqun): Change the target to crf_decode
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
return
batch_id = batch_id + 1
def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in xrange(PASS_NUM):
chunk_evaluator.reset(exe)
for data in train_data():
cost, precision, recall, f1_score = exe.run(
main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost] + chunk_evaluator.metrics)
pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
exe)
if batch_id % 10 == 0:
print("avg_cost:" + str(cost) + " precision:" +
str(precision) + " recall:" + str(recall) +
" f1_score:" + str(f1_score) + " pass_precision:" +
str(pass_precision) + " pass_recall:" +
str(pass_recall) + " pass_f1_score:" +
str(pass_f1_score))
if batch_id != 0:
print("second per batch: " +
str((time.time() - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(pass_precision) > 0.01:
if save_dirname is not None:
# TODO(liuyiqun): Change the target to crf_decode
fluid.io.save_inference_model(
save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
return
batch_id = batch_id + 1
batch_size=BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder = fluid.DataFeeder(feed_list=[
word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
],
place=place)
exe = fluid.Executor(place)
main_program = fluid.default_main_program()
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim), place)
start_time = time.time()
batch_id = 0
for pass_id in six.moves.xrange(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost: " + str(cost))
if batch_id != 0:
print("second per batch: " +
str((time.time() - start_time) / batch_id))