提交 797e89ec 编写于 作者: Y Yu Yang

Merge branch 'develop' of github.com:baidu/Paddle into feature/better_infer_interface

...@@ -14,7 +14,7 @@ ...@@ -14,7 +14,7 @@
INCLUDE(ExternalProject) INCLUDE(ExternalProject)
FIND_PACKAGE(Protobuf) FIND_PACKAGE(Protobuf 3.1)
IF(NOT PROTOBUF_FOUND) IF(NOT PROTOBUF_FOUND)
SET(PROTOBUF_SOURCES_DIR ${THIRD_PARTY_PATH}/protobuf) SET(PROTOBUF_SOURCES_DIR ${THIRD_PARTY_PATH}/protobuf)
......
...@@ -92,12 +92,8 @@ def main(): ...@@ -92,12 +92,8 @@ def main():
def event_handler(event): def event_handler(event):
if isinstance(event, paddle.event.EndIteration): if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1000 == 0: if event.batch_id % 1000 == 0:
result = trainer.test(reader=paddle.batch( print "Pass %d, Batch %d, Cost %f, %s" % (
paddle.dataset.mnist.test(), batch_size=256)) event.pass_id, event.batch_id, event.cost, event.metrics)
print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics,
result.metrics)
with gzip.open('params.tar.gz', 'w') as f: with gzip.open('params.tar.gz', 'w') as f:
parameters.to_tar(f) parameters.to_tar(f)
......
import paddle.v2 as paddle
import cPickle
import copy
def main():
paddle.init(use_gpu=False)
movie_title_dict = paddle.dataset.movielens.get_movie_title_dict()
uid = paddle.layer.data(
name='user_id',
type=paddle.data_type.integer_value(
paddle.dataset.movielens.max_user_id() + 1))
usr_emb = paddle.layer.embedding(input=uid, size=32)
usr_gender_id = paddle.layer.data(
name='gender_id', type=paddle.data_type.integer_value(2))
usr_gender_emb = paddle.layer.embedding(input=usr_gender_id, size=16)
usr_age_id = paddle.layer.data(
name='age_id',
type=paddle.data_type.integer_value(
len(paddle.dataset.movielens.age_table)))
usr_age_emb = paddle.layer.embedding(input=usr_age_id, size=16)
usr_job_id = paddle.layer.data(
name='job_id',
type=paddle.data_type.integer_value(paddle.dataset.movielens.max_job_id(
) + 1))
usr_job_emb = paddle.layer.embedding(input=usr_job_id, size=16)
usr_combined_features = paddle.layer.fc(
input=[usr_emb, usr_gender_emb, usr_age_emb, usr_job_emb],
size=200,
act=paddle.activation.Tanh())
mov_id = paddle.layer.data(
name='movie_id',
type=paddle.data_type.integer_value(
paddle.dataset.movielens.max_movie_id() + 1))
mov_emb = paddle.layer.embedding(input=mov_id, size=32)
mov_categories = paddle.layer.data(
name='category_id',
type=paddle.data_type.sparse_binary_vector(
len(paddle.dataset.movielens.movie_categories())))
mov_categories_hidden = paddle.layer.fc(input=mov_categories, size=32)
mov_title_id = paddle.layer.data(
name='movie_title',
type=paddle.data_type.integer_value_sequence(len(movie_title_dict)))
mov_title_emb = paddle.layer.embedding(input=mov_title_id, size=32)
mov_title_conv = paddle.networks.sequence_conv_pool(
input=mov_title_emb, hidden_size=32, context_len=3)
mov_combined_features = paddle.layer.fc(
input=[mov_emb, mov_categories_hidden, mov_title_conv],
size=200,
act=paddle.activation.Tanh())
inference = paddle.layer.cos_sim(
a=usr_combined_features, b=mov_combined_features, size=1, scale=5)
cost = paddle.layer.regression_cost(
input=inference,
label=paddle.layer.data(
name='score', type=paddle.data_type.dense_vector(1)))
parameters = paddle.parameters.create(cost)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=paddle.optimizer.Adam(
learning_rate=1e-4))
feeding = {
'user_id': 0,
'gender_id': 1,
'age_id': 2,
'job_id': 3,
'movie_id': 4,
'category_id': 5,
'movie_title': 6,
'score': 7
}
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d Batch %d Cost %.2f" % (
event.pass_id, event.batch_id, event.cost)
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=256),
event_handler=event_handler,
feeding=feeding,
num_passes=1)
user_id = 234
movie_id = 345
user = paddle.dataset.movielens.user_info()[user_id]
movie = paddle.dataset.movielens.movie_info()[movie_id]
feature = user.value() + movie.value()
def reader():
yield feature
infer_dict = copy.copy(feeding)
del infer_dict['score']
prediction = paddle.infer(
output=inference,
parameters=parameters,
reader=paddle.batch(
reader, batch_size=32),
feeding=infer_dict)
print(prediction + 5) / 2
if __name__ == '__main__':
main()
...@@ -85,6 +85,9 @@ class DataFeeder(DataProviderConverter): ...@@ -85,6 +85,9 @@ class DataFeeder(DataProviderConverter):
input_types.append(each[1]) input_types.append(each[1])
DataProviderConverter.__init__(self, input_types) DataProviderConverter.__init__(self, input_types)
def __len__(self):
return len(self.input_names)
def convert(self, dat, argument=None): def convert(self, dat, argument=None):
""" """
:param dat: A list of mini-batch data. Each sample is a list or tuple :param dat: A list of mini-batch data. Each sample is a list or tuple
......
...@@ -23,7 +23,12 @@ import re ...@@ -23,7 +23,12 @@ import re
import random import random
import functools import functools
__all__ = ['train_creator', 'test_creator'] __all__ = [
'train', 'test', 'get_movie_title_dict', 'max_movie_id', 'max_user_id',
'age_table', 'movie_categories', 'max_job_id', 'user_info', 'movie_info'
]
age_table = [1, 18, 25, 35, 45, 50, 56]
class MovieInfo(object): class MovieInfo(object):
...@@ -38,17 +43,32 @@ class MovieInfo(object): ...@@ -38,17 +43,32 @@ class MovieInfo(object):
[MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()] [MOVIE_TITLE_DICT[w.lower()] for w in self.title.split()]
] ]
def __str__(self):
return "<MovieInfo id(%d), title(%s), categories(%s)>" % (
self.index, self.title, self.categories)
def __repr__(self):
return self.__str__()
class UserInfo(object): class UserInfo(object):
def __init__(self, index, gender, age, job_id): def __init__(self, index, gender, age, job_id):
self.index = int(index) self.index = int(index)
self.is_male = gender == 'M' self.is_male = gender == 'M'
self.age = [1, 18, 25, 35, 45, 50, 56].index(int(age)) self.age = age_table.index(int(age))
self.job_id = int(job_id) self.job_id = int(job_id)
def value(self): def value(self):
return [self.index, 0 if self.is_male else 1, self.age, self.job_id] return [self.index, 0 if self.is_male else 1, self.age, self.job_id]
def __str__(self):
return "<UserInfo id(%d), gender(%s), age(%d), job(%d)>" % (
self.index, "M"
if self.is_male else "F", age_table[self.age], self.job_id)
def __repr__(self):
return str(self)
MOVIE_INFO = None MOVIE_INFO = None
MOVIE_TITLE_DICT = None MOVIE_TITLE_DICT = None
...@@ -59,7 +79,8 @@ USER_INFO = None ...@@ -59,7 +79,8 @@ USER_INFO = None
def __initialize_meta_info__(): def __initialize_meta_info__():
fn = download( fn = download(
url='http://files.grouplens.org/datasets/movielens/ml-1m.zip', url='http://files.grouplens.org/datasets/movielens/ml-1m.zip',
md5='c4d9eecfca2ab87c1945afe126590906') module_name='movielens',
md5sum='c4d9eecfca2ab87c1945afe126590906')
global MOVIE_INFO global MOVIE_INFO
if MOVIE_INFO is None: if MOVIE_INFO is None:
pattern = re.compile(r'^(.*)\((\d+)\)$') pattern = re.compile(r'^(.*)\((\d+)\)$')
...@@ -122,14 +143,63 @@ def __reader_creator__(**kwargs): ...@@ -122,14 +143,63 @@ def __reader_creator__(**kwargs):
return lambda: __reader__(**kwargs) return lambda: __reader__(**kwargs)
train_creator = functools.partial(__reader_creator__, is_test=False) train = functools.partial(__reader_creator__, is_test=False)
test_creator = functools.partial(__reader_creator__, is_test=True) test = functools.partial(__reader_creator__, is_test=True)
def get_movie_title_dict():
__initialize_meta_info__()
return MOVIE_TITLE_DICT
def __max_index_info__(a, b):
if a.index > b.index:
return a
else:
return b
def max_movie_id():
__initialize_meta_info__()
return reduce(__max_index_info__, MOVIE_INFO.viewvalues()).index
def max_user_id():
__initialize_meta_info__()
return reduce(__max_index_info__, USER_INFO.viewvalues()).index
def __max_job_id_impl__(a, b):
if a.job_id > b.job_id:
return a
else:
return b
def max_job_id():
__initialize_meta_info__()
return reduce(__max_job_id_impl__, USER_INFO.viewvalues()).job_id
def movie_categories():
__initialize_meta_info__()
return CATEGORIES_DICT
def user_info():
__initialize_meta_info__()
return USER_INFO
def movie_info():
__initialize_meta_info__()
return MOVIE_INFO
def unittest(): def unittest():
for train_count, _ in enumerate(train_creator()()): for train_count, _ in enumerate(train()()):
pass pass
for test_count, _ in enumerate(test_creator()()): for test_count, _ in enumerate(test()()):
pass pass
print train_count, test_count print train_count, test_count
......
...@@ -23,7 +23,7 @@ class Inference(object): ...@@ -23,7 +23,7 @@ class Inference(object):
def iter_infer(self, input=None, batch_size=None, reader=None, def iter_infer(self, input=None, batch_size=None, reader=None,
feeding=None): feeding=None):
feeder = DataFeeder(self.__data_types__, feeding)
if reader is None: if reader is None:
assert input is not None and isinstance(input, collections.Iterable) assert input is not None and isinstance(input, collections.Iterable)
if not isinstance(input, collections.Iterable): if not isinstance(input, collections.Iterable):
...@@ -45,8 +45,6 @@ class Inference(object): ...@@ -45,8 +45,6 @@ class Inference(object):
if input is not None: if input is not None:
raise ValueError("User should set either input or reader, " raise ValueError("User should set either input or reader, "
"should not set them both.") "should not set them both.")
feeder = DataFeeder(self.__data_types__, feeding)
self.__gradient_machine__.start() self.__gradient_machine__.start()
for data_batch in reader(): for data_batch in reader():
yield self.__gradient_machine__.forwardTest(feeder(data_batch)) yield self.__gradient_machine__.forwardTest(feeder(data_batch))
...@@ -70,7 +68,7 @@ class Inference(object): ...@@ -70,7 +68,7 @@ class Inference(object):
return retv return retv
def infer(output_layer, parameters, input=None, feeding=None, field='value'): def infer(output_layer, parameters, input, feeding=None, field='value'):
""" """
Infer a neural network by given neural network output and parameters. The Infer a neural network by given neural network output and parameters. The
user should pass either a batch of input data or reader method. user should pass either a batch of input data or reader method.
......
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