def __init__(self,
dnn_dims=[],
vocab_sizes=[],
model_type=ModelType.create_classification(),
model_arch=ModelArch.create_rnn(),
share_semantic_generator=False,
class_num=2,
share_embed=False,
is_infer=False):
"""
init dssm network
:param dnn_dims: list of int (dimentions of each layer in semantic vector generator.)
:param vocab_sizes: 2d tuple (size of both left and right items.)
:param model_type: classification
:param model_arch: model architecture
:param share_semantic_generator: bool (whether to share the semantic vector generator for both left and right.)
:param class_num: number of categories.
:param share_embed: bool (whether to share the embeddings between left and right.)
:param is_infer: inference
"""
assert len(vocab_sizes) == 2, (
"vocab sizes specify the sizes left and right inputs, dim is 2.")
assert len(dnn_dims) > 1, "more than two layers is needed."
self.dnn_dims = dnn_dims
self.vocab_sizes = vocab_sizes
self.share_semantic_generator = share_semantic_generator
self.share_embed = share_embed
self.model_type = ModelType(model_type)
self.model_arch = ModelArch(model_arch)
self.class_num = class_num
self.is_infer = is_infer
logger.warning("build DSSM model with config of %s, %s" %
(self.model_type, self.model_arch))
logger.info("vocabulary sizes: %s" % str(self.vocab_sizes))
_model_arch = {
"rnn": self.create_rnn,
"cnn": self.create_cnn,
"fc": self.create_fc,
}
def _model_arch_creater(emb, prefix=""):
sent_vec = _model_arch.get(str(model_arch))(emb, prefix)
dnn = self.create_dnn(sent_vec, prefix)
return dnn
self.model_arch_creater = _model_arch_creater
self.model_type_creater = self._build_classification_model
def infer(self, data_path, output_path,
model_type=ModelType(ModelType.CLASSIFICATION_MODE),
feature_dim=800,
batch_size=100):
logger.info("infer data...")
#infer_reader = reader.test(data_path,
# feature_dim+1,
# model_type.is_classification())
infer_batch = paddle.batch(reader.test(data_path,
feature_dim+1,
model_type.is_classification()),
batch_size=batch_size)
logger.warning('write predictions to %s' % output_path)
output_f = open(output_path, 'w')
batch = []
#for item in infer_reader():
# batch.append([item[0]])
for id, batch in enumerate(infer_batch()):
res = self.inferer.infer(input=batch)
predictions = [' '.join(map(str, x)) for x in res]
assert len(batch) == len(
predictions), "predict error, %d inputs, but %d predictions" % (
len(batch), len(predictions))
output_f.write('\n'.join(map(str, predictions)) + '\n')
batch = []
def __init__(self, param_path,
model_type=ModelType(ModelType.CLASSIFICATION_MODE),
class_num=2,
feature_dim=800,
dnn_dims='256,128,64,32'):
logger.info("create DNN model")
paddle.init(use_gpu=False, trainer_count=1)
# network config
input_layer = paddle.layer.data(name='input_layer', type=paddle.data_type.dense_vector(feature_dim))
layer_dims = [int(i) for i in dnn_dims.split(',')]
dnn = create_dnn(input_layer, layer_dims)
prediction = None
label = None
cost = None
if model_type.is_classification():
prediction = paddle.layer.fc(input=dnn, size=class_num, act=paddle.activation.Softmax())
label = paddle.layer.data(name='label', type=paddle.data_type.integer_value(class_num))
cost = paddle.layer.classification_cost(input=prediction, label=label)
elif model_type.is_regression():
prediction = paddle.layer.fc(input=dnn, size=1, act=paddle.activation.Linear())
label = paddle.layer.data(name='label', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.mse_cost(input=prediction, label=label)
# load parameter
logger.info("load model parameters from %s" % param_path)
self.parameters = paddle.parameters.Parameters.from_tar(
open(param_path, 'r'))
self.inferer = paddle.inference.Inference(
output_layer=prediction, parameters=self.parameters)
def __init__(self,
dnn_layer_dims,
dnn_input_dim,
lr_input_dim,
model_type=ModelType.create_classification(),
is_infer=False):
'''
@dnn_layer_dims: list of integer
dims of each layer in dnn
@dnn_input_dim: int
size of dnn's input layer
@lr_input_dim: int
size of lr's input layer
@is_infer: bool
whether to build a infer model
'''
self.dnn_layer_dims = dnn_layer_dims
self.dnn_input_dim = dnn_input_dim
self.lr_input_dim = lr_input_dim
self.model_type = model_type
self.is_infer = is_infer
self._declare_input_layers()
self.dnn = self._build_dnn_submodel_(self.dnn_layer_dims)
self.lr = self._build_lr_submodel_()
# model's prediction
# TODO(superjom) rename it to prediction
if self.model_type.is_classification():
self.model = self._build_classification_model(self.dnn, self.lr)
if self.model_type.is_regression():
self.model = self._build_regression_model(self.dnn, self.lr)
def __init__(self,
dnn_layer_dims,
dnn_input_dim,
lr_input_dim,
model_type=ModelType.create_classification(),
is_infer=False):
'''
@dnn_layer_dims: list of integer
DNN每一层的维度
@dnn_input_dim: int
DNN输入层的大小
@lr_input_dim: int
LR输入层大小
@is_infer: bool
是否建立预估模型
'''
self.dnn_layer_dims = dnn_layer_dims
self.dnn_input_dim = dnn_input_dim
self.lr_input_dim = lr_input_dim
self.model_type = model_type
self.is_infer = is_infer
self._declare_input_layers()
self.dnn = self._build_dnn_submodel_(self.dnn_layer_dims)
self.lr = self._build_lr_submodel_()
# 模型预测
if self.model_type.is_classification():
self.model = self._build_classification_model(self.dnn, self.lr)
if self.model_type.is_regression():
self.model = self._build_regression_model(self.dnn, self.lr)
def __init__(self, train_path, test_path, source_dic_path, target_dic_path,
model_type):
self.train_path = train_path
self.test_path = test_path
self.source_dic_path = source_dic_path
self.target_dic_path = target_dic_path
self.model_type = ModelType(model_type)
self.source_dic = load_dic(self.source_dic_path)
self.target_dic = load_dic(self.target_dic_path)
_record_reader = {
ModelType.CLASSIFICATION_MODE: self._read_classification_record,
ModelType.REGRESSION_MODE: self._read_regression_record,
ModelType.RANK_MODE: self._read_rank_record,
}
assert isinstance(model_type, ModelType)
self.record_reader = _record_reader[model_type.mode]
self.is_infer = False
self.train_data_csv = "/home/kesci/input/qichedashi/train_set.csv"
self.dev_data_csv = "/home/kesci/input/qichedashi/final_round_dev_set.csv"
self.test_data_csv = "/home/kesci/input/qichedashi/final_round_test_set.csv"
self.NEG = 3
self.train_samples = 200000
def train():
args = parse_args()
args.model_type = ModelType(
args.model_type) #--model_type=0,1 classification regression
#只使用cpu而且cpu只开一个线程
paddle.init(use_gpu=False, trainer_count=1)
'''dnn_input_dim: 61
lr_input_dim: 10040001'''
dnn_input_dim, lr_input_dim = reader.load_data_meta(args.data_meta_file)
# create ctr model.
model = CTRmodel(dnn_layer_dims,
dnn_input_dim,
lr_input_dim,
model_type=args.model_type,
is_infer=False)
params = paddle.parameters.create(model.train_cost)
optimizer = paddle.optimizer.AdaGrad() #学习率优化
trainer = paddle.trainer.SGD(cost=model.train_cost,
parameters=params,
update_equation=optimizer)
dataset = reader.Dataset()
def __event_handler__(event):
if isinstance(event, paddle.event.EndIteration):
num_samples = event.batch_id * args.batch_size
if event.batch_id % 100 == 0:
logger.warning(
"Pass %d, Samples %d, Cost %f, %s" %
(event.pass_id, num_samples, event.cost, event.metrics))
if event.batch_id % 1000 == 0:
if args.test_data_path:
result = trainer.test(reader=paddle.batch(
dataset.test(args.test_data_path),
batch_size=args.batch_size),
feeding=reader.feeding_index)
logger.warning("Test %d-%d, Cost %f, %s" %
(event.pass_id, event.batch_id, result.cost,
result.metrics))
path = "{}-pass-{}-batch-{}-test-{}.tar.gz".format(
args.model_output_prefix, event.pass_id, event.batch_id,
result.cost)
with gzip.open(path, 'w') as f:
trainer.save_parameter_to_tar(f)
trainer.train(reader=paddle.batch(paddle.reader.shuffle(dataset.train(
args.train_data_path),
buf_size=500),
batch_size=args.batch_size),
feeding=reader.feeding_index,
event_handler=__event_handler__,
num_passes=args.num_passes)
def __init__(self, param_path):
logger.info("create CTR model")
dnn_input_dim, lr_input_dim = reader.load_data_meta(args.data_meta_path)
# create the mdoel
self.ctr_model = network_conf.CTRmodel(
dnn_layer_dims,
dnn_input_dim,
lr_input_dim,
model_type=ModelType(args.model_type),
is_infer=True)
# load parameter
logger.info("load model parameters from %s" % param_path)
self.parameters = paddle.parameters.Parameters.from_tar(
gzip.open(param_path, 'r'))
self.inferer = paddle.inference.Inference(
output_layer=self.ctr_model.model,
parameters=self.parameters, )
def __init__(self, train_path, test_path, source_dic_path, target_dic_path,
model_type):
self.train_path = train_path
self.test_path = test_path
self.source_dic_path = source_dic_path
self.target_dic_path = target_dic_path
self.model_type = ModelType(model_type)
self.source_dic = load_dic(self.source_dic_path)
self.target_dic = load_dic(self.target_dic_path)
_record_reader = {
ModelType.CLASSIFICATION_MODE: self._read_classification_record,
ModelType.REGRESSION_MODE: self._read_regression_record,
ModelType.RANK_MODE: self._read_rank_record,
}
assert isinstance(model_type, ModelType)
self.record_reader = _record_reader[model_type.mode]
self.is_infer = False
def train(data_path=None,
model_type=ModelType.create_classification(),
batch_size=100,
num_passes=50,
class_num=None,
num_workers=1,
use_gpu=False):
'''
Train the DNN.
'''
paddle.init(use_gpu=use_gpu, trainer_count=num_workers)
# network config
input_layer = paddle.layer.data(name='input_layer', type=paddle.data_type.dense_vector(feature_dim))
dnn = create_dnn(input_layer)
prediction = None
label = None
cost = None
if args.model_type.is_classification():
prediction = paddle.layer.fc(input=dnn, size=class_num, act=paddle.activation.Softmax())
label = paddle.layer.data(name='label', type=paddle.data_type.integer_value(class_num))
cost = paddle.layer.classification_cost(input=prediction, label=label)
elif args.model_type.is_regression():
prediction = paddle.layer.fc(input=dnn, size=1, act=paddle.activation.Linear())
label = paddle.layer.data(name='label', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.mse_cost(input=prediction, label=label)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer
optimizer = paddle.optimizer.Momentum(momentum=0)
trainer = paddle.trainer.SGD(
cost=cost,
extra_layers=paddle.evaluator.auc(input=prediction, label=label),
parameters=parameters, update_equation=optimizer)
feeding = {'input_layer': 0, 'label': 1}
# event_handler to print training and testing info
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.batch(reader.test(data_path,
feature_dim+1,
args.model_type.is_classification()),
batch_size=batch_size),
feeding=feeding)
print "Test %d, Cost %f, %s" % (event.pass_id, result.cost, result.metrics)
model_desc = "{type}".format(
type=str(args.model_type))
with open("%sdnn_%s_pass_%05d.tar" %
(args.model_output_prefix, model_desc,
event.pass_id), "w") as f:
parameters.to_tar(f)
# training
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(reader.train(data_path,
feature_dim+1,
args.model_type.is_classification()),
buf_size=batch_size*10),
batch_size=batch_size),
feeding=feeding,
event_handler=event_handler,
num_passes=num_passes)
'-c',
'--class_num',
type=int,
default=0,
help="number of categories for classification task.")
parser.add_argument(
'--num_workers', type=int, default=1, help="num worker threads, default 1")
parser.add_argument(
'--use_gpu',
type=bool,
default=False,
help="whether to use GPU devices (default: False)")
# arguments check.
args = parser.parse_args()
args.model_type = ModelType(args.model_type)
if args.model_type.is_classification():
assert args.class_num > 1, "--class_num should be set in classification task."
feature_dim = args.feature_dim
layer_dims = [int(i) for i in args.dnn_dims.split(',')]
def create_dnn(sent_vec):
# if more than three layers, than a fc layer will be added.
if len(layer_dims) > 1:
_input_layer = sent_vec
for id, dim in enumerate(layer_dims):
name = "fc_%d_%d" % (id, dim)
logger.info("create fc layer [%s] which dimention is %d" %
(name, dim))
fc = paddle.layer.fc(
def __init__(self,
dnn_dims=[],
vocab_sizes=[],
model_type=ModelType.create_classification(),
model_arch=ModelArch.create_cnn(),
share_semantic_generator=False,
class_num=None,
share_embed=False,
is_infer=False):
"""
:param dnn_dims: The dimention of each layer in the semantic vector
generator.
:type dnn_dims: list of int
:param vocab_sizes: The size of left and right items.
:type vocab_sizes: A list having 2 elements.
:param model_type: The type of task to train the DSSM model. The value
should be "rank: 0", "regression: 1" or
"classification: 2".
:type model_type: int
:param model_arch: A value indicating the model architecture to use.
:type model_arch: int
:param share_semantic_generator: A flag indicating whether to share the
semantic vector between the left and
the right item.
:type share_semantic_generator: bool
:param share_embed: A floag indicating whether to share the embeddings
between the left and the right item.
:type share_embed: bool
:param class_num: The number of categories.
:type class_num: int
"""
assert len(vocab_sizes) == 2, (
"The vocab_sizes specifying the sizes left and right inputs. "
"Its dimension should be 2.")
assert len(dnn_dims) > 1, ("In the DNN model, more than two layers "
"are needed.")
self.dnn_dims = dnn_dims
self.vocab_sizes = vocab_sizes
self.share_semantic_generator = share_semantic_generator
self.share_embed = share_embed
self.model_type = ModelType(model_type)
self.model_arch = ModelArch(model_arch)
self.class_num = class_num
self.is_infer = is_infer
logger.warning("Build DSSM model with config of %s, %s" %
(self.model_type, self.model_arch))
logger.info("The vocabulary size is : %s" % str(self.vocab_sizes))
# bind model architecture
_model_arch = {
"cnn": self.create_cnn,
"fc": self.create_fc,
"rnn": self.create_rnn,
}
def _model_arch_creater(emb, prefix=""):
sent_vec = _model_arch.get(str(model_arch))(emb, prefix)
dnn = self.create_dnn(sent_vec, prefix)
return dnn
self.model_arch_creater = _model_arch_creater
_model_type = {
"classification": self._build_classification_model,
"rank": self._build_rank_model,
"regression": self._build_regression_model,
}
print("model type: ", str(self.model_type))
self.model_type_creater = _model_type[str(self.model_type)]
class DSSM(object):
def __init__(self,
dnn_dims=[],
vocab_sizes=[],
model_type=ModelType.create_classification(),
model_arch=ModelArch.create_cnn(),
share_semantic_generator=False,
class_num=None,
share_embed=False,
is_infer=False):
"""
:param dnn_dims: The dimention of each layer in the semantic vector
generator.
:type dnn_dims: list of int
:param vocab_sizes: The size of left and right items.
:type vocab_sizes: A list having 2 elements.
:param model_type: The type of task to train the DSSM model. The value
should be "rank: 0", "regression: 1" or
"classification: 2".
:type model_type: int
:param model_arch: A value indicating the model architecture to use.
:type model_arch: int
:param share_semantic_generator: A flag indicating whether to share the
semantic vector between the left and
the right item.
:type share_semantic_generator: bool
:param share_embed: A floag indicating whether to share the embeddings
between the left and the right item.
:type share_embed: bool
:param class_num: The number of categories.
:type class_num: int
"""
assert len(vocab_sizes) == 2, (
"The vocab_sizes specifying the sizes left and right inputs. "
"Its dimension should be 2.")
assert len(dnn_dims) > 1, ("In the DNN model, more than two layers "
"are needed.")
self.dnn_dims = dnn_dims
self.vocab_sizes = vocab_sizes
self.share_semantic_generator = share_semantic_generator
self.share_embed = share_embed
self.model_type = ModelType(model_type)
self.model_arch = ModelArch(model_arch)
self.class_num = class_num
self.is_infer = is_infer
logger.warning("Build DSSM model with config of %s, %s" %
(self.model_type, self.model_arch))
logger.info("The vocabulary size is : %s" % str(self.vocab_sizes))
# bind model architecture
_model_arch = {
"cnn": self.create_cnn,
"fc": self.create_fc,
"rnn": self.create_rnn,
}
def _model_arch_creater(emb, prefix=""):
sent_vec = _model_arch.get(str(model_arch))(emb, prefix)
dnn = self.create_dnn(sent_vec, prefix)
return dnn
self.model_arch_creater = _model_arch_creater
_model_type = {
"classification": self._build_classification_model,
"rank": self._build_rank_model,
"regression": self._build_regression_model,
}
print("model type: ", str(self.model_type))
self.model_type_creater = _model_type[str(self.model_type)]
def __call__(self):
return self.model_type_creater()
def create_embedding(self, input, prefix=""):
"""
Create word embedding. The `prefix` is added in front of the name of
embedding"s learnable parameter.
"""
logger.info("Create embedding table [%s] whose dimention is %d. " %
(prefix, self.dnn_dims[0]))
emb = paddle.layer.embedding(input=input,
size=self.dnn_dims[0],
param_attr=ParamAttr(name="%s_emb.w" %
prefix))
return emb
def create_fc(self, emb, prefix=""):
"""
A multi-layer fully connected neural networks.
:param emb: The output of the embedding layer
:type emb: paddle.layer
:param prefix: A prefix will be added to the layers' names.
:type prefix: str
"""
_input_layer = paddle.layer.pooling(input=emb,
pooling_type=paddle.pooling.Max())
fc = paddle.layer.fc(input=_input_layer,
size=self.dnn_dims[1],
param_attr=ParamAttr(name="%s_fc.w" % prefix),
bias_attr=ParamAttr(name="%s_fc.b" % prefix,
initial_std=0.))
return fc
def create_rnn(self, emb, prefix=""):
"""
A GRU sentence vector learner.
"""
gru = paddle.networks.simple_gru(
input=emb,
size=self.dnn_dims[1],
mixed_param_attr=ParamAttr(name="%s_gru_mixed.w" % prefix),
mixed_bias_param_attr=ParamAttr(name="%s_gru_mixed.b" % prefix),
gru_param_attr=ParamAttr(name="%s_gru.w" % prefix),
gru_bias_attr=ParamAttr(name="%s_gru.b" % prefix))
sent_vec = paddle.layer.last_seq(gru)
return sent_vec
def create_cnn(self, emb, prefix=""):
"""
A multi-layer CNN.
:param emb: The word embedding.
:type emb: paddle.layer
:param prefix: The prefix will be added to of layers' names.
:type prefix: str
"""
def create_conv(context_len, hidden_size, prefix):
key = "%s_%d_%d" % (prefix, context_len, hidden_size)
conv = paddle.networks.sequence_conv_pool(
input=emb,
context_len=context_len,
hidden_size=hidden_size,
# set parameter attr for parameter sharing
context_proj_param_attr=ParamAttr(name=key + "contex_proj.w"),
fc_param_attr=ParamAttr(name=key + "_fc.w"),
fc_bias_attr=ParamAttr(name=key + "_fc.b"),
pool_bias_attr=ParamAttr(name=key + "_pool.b"))
return conv
logger.info("create a sequence_conv_pool whose context width is 3.")
conv_3 = create_conv(3, self.dnn_dims[1], "cnn")
logger.info("create a sequence_conv_pool whose context width is 4.")
conv_4 = create_conv(4, self.dnn_dims[1], "cnn")
return paddle.layer.concat(input=[conv_3, conv_4])
def create_dnn(self, sent_vec, prefix):
# if more than three layers, than a fc layer will be added.
if len(self.dnn_dims) > 1:
_input_layer = sent_vec
for id, dim in enumerate(self.dnn_dims[1:]):
name = "%s_fc_%d_%d" % (prefix, id, dim)
logger.info("create fc layer [%s] which dimention is %d" %
(name, dim))
fc = paddle.layer.fc(input=_input_layer,
size=dim,
act=paddle.activation.Tanh(),
param_attr=ParamAttr(name="%s.w" % name),
bias_attr=ParamAttr(name="%s.b" % name,
initial_std=0.))
_input_layer = fc
return _input_layer
def _build_classification_model(self):
logger.info("build classification model")
assert self.model_type.is_classification()
return self._build_classification_or_regression_model(
is_classification=True)
def _build_regression_model(self):
logger.info("build regression model")
assert self.model_type.is_regression()
return self._build_classification_or_regression_model(
is_classification=False)
def _build_rank_model(self):
"""
Build a pairwise rank model, and the cost is returned.
A pairwise rank model has 3 inputs:
- source sentence
- left_target sentence
- right_target sentence
- label, 1 if left_target should be sorted in front of
right_target, otherwise 0.
"""
logger.info("build rank model")
assert self.model_type.is_rank()
source = paddle.layer.data(
name="source_input",
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[0]))
left_target = paddle.layer.data(
name="left_target_input",
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[1]))
right_target = paddle.layer.data(
name="right_target_input",
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[1]))
if not self.is_infer:
label = paddle.layer.data(name="label_input",
type=paddle.data_type.integer_value(1))
prefixs = "_ _ _".split(
) if self.share_semantic_generator else "source target target".split()
embed_prefixs = "_ _ _".split(
) if self.share_embed else "source target target".split()
word_vecs = []
for id, input in enumerate([source, left_target, right_target]):
x = self.create_embedding(input, prefix=embed_prefixs[id])
word_vecs.append(x)
semantics = []
for id, input in enumerate(word_vecs):
x = self.model_arch_creater(input, prefix=prefixs[id])
semantics.append(x)
# The cosine similarity score of source and left_target.
left_score = paddle.layer.cos_sim(semantics[0], semantics[1])
# The cosine similarity score of source and right target.
right_score = paddle.layer.cos_sim(semantics[0], semantics[2])
if not self.is_infer:
# rank cost
cost = paddle.layer.rank_cost(left_score, right_score, label=label)
# prediction = left_score - right_score
# but this operator is not supported currently.
# so AUC will not used.
return cost, None, label
return right_score
def _build_classification_or_regression_model(self, is_classification):
"""
Build a classification/regression model, and the cost is returned.
The classification/regression task expects 3 inputs:
- source sentence
- target sentence
- classification label
"""
if is_classification:
assert self.class_num
source = paddle.layer.data(
name="source_input",
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[0]))
target = paddle.layer.data(
name="target_input",
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[1]))
label = paddle.layer.data(
name="label_input",
type=paddle.data_type.integer_value(self.class_num)
if is_classification else paddle.data_type.dense_vector(1))
prefixs = "_ _".split(
) if self.share_semantic_generator else "source target".split()
embed_prefixs = "_ _".split(
) if self.share_embed else "source target".split()
word_vecs = []
for id, input in enumerate([source, target]):
x = self.create_embedding(input, prefix=embed_prefixs[id])
word_vecs.append(x)
semantics = []
for id, input in enumerate(word_vecs):
x = self.model_arch_creater(input, prefix=prefixs[id])
semantics.append(x)
if is_classification:
concated_vector = paddle.layer.concat(semantics)
prediction = paddle.layer.fc(input=concated_vector,
size=self.class_num,
act=paddle.activation.Softmax())
cost = paddle.layer.classification_cost(input=prediction,
label=label)
else:
prediction = paddle.layer.cos_sim(*semantics)
cost = paddle.layer.square_error_cost(prediction, label)
if not self.is_infer:
return cost, prediction, label
return prediction
def train(train_data_path=None,
test_data_path=None,
source_dic_path=None,
target_dic_path=None,
model_type=ModelType.create_classification(),
model_arch=ModelArch.create_cnn(),
batch_size=10,
num_passes=10,
share_semantic_generator=False,
share_embed=False,
class_num=None,
num_workers=1,
use_gpu=False):
'''
Train the DSSM.
'''
default_train_path = './data/rank/train.txt'
default_test_path = './data/rank/test.txt'
default_dic_path = './data/vocab.txt'
if not model_type.is_rank():
default_train_path = './data/classification/train.txt'
default_test_path = './data/classification/test.txt'
use_default_data = not train_data_path
if use_default_data:
train_data_path = default_train_path
test_data_path = default_test_path
source_dic_path = default_dic_path
target_dic_path = default_dic_path
dataset = reader.Dataset(
train_path=train_data_path,
test_path=test_data_path,
source_dic_path=source_dic_path,
target_dic_path=target_dic_path,
model_type=model_type,
)
train_reader = paddle.batch(paddle.reader.shuffle(dataset.train,
buf_size=1000),
batch_size=batch_size)
test_reader = paddle.batch(paddle.reader.shuffle(dataset.test,
buf_size=1000),
batch_size=batch_size)
paddle.init(use_gpu=use_gpu, trainer_count=num_workers)
cost, prediction, label = DSSM(
dnn_dims=layer_dims,
vocab_sizes=[
len(load_dic(path)) for path in [source_dic_path, target_dic_path]
],
model_type=model_type,
model_arch=model_arch,
share_semantic_generator=share_semantic_generator,
class_num=class_num,
share_embed=share_embed)()
parameters = paddle.parameters.create(cost)
adam_optimizer = paddle.optimizer.Adam(
learning_rate=1e-3,
regularization=paddle.optimizer.L2Regularization(rate=1e-3),
model_average=paddle.optimizer.ModelAverage(average_window=0.5))
trainer = paddle.trainer.SGD(
cost=cost,
extra_layers=paddle.evaluator.auc(input=prediction, label=label)
if not model_type.is_rank() else None,
parameters=parameters,
update_equation=adam_optimizer)
feeding = {}
if model_type.is_classification() or model_type.is_regression():
feeding = {'source_input': 0, 'target_input': 1, 'label_input': 2}
else:
feeding = {
'source_input': 0,
'left_target_input': 1,
'right_target_input': 2,
'label_input': 3
}
def _event_handler(event):
'''
Define batch handler
'''
if isinstance(event, paddle.event.EndIteration):
# output train log
if event.batch_id % args.num_batches_to_log == 0:
logger.info(
"Pass %d, Batch %d, Cost %f, %s" %
(event.pass_id, event.batch_id, event.cost, event.metrics))
# test model
if event.batch_id > 0 and event.batch_id % args.num_batches_to_test == 0:
if test_reader is not None:
if model_type.is_classification():
result = trainer.test(reader=test_reader,
feeding=feeding)
logger.info("Test at Pass %d, %s" %
(event.pass_id, result.metrics))
else:
result = None
# save model
if event.batch_id > 0 and event.batch_id % args.num_batches_to_save_model == 0:
model_desc = "{type}_{arch}".format(type=str(args.model_type),
arch=str(args.model_arch))
with open(
"%sdssm_%s_pass_%05d.tar" %
(args.model_output_prefix, model_desc, event.pass_id),
"w") as f:
parameters.to_tar(f)
trainer.train(reader=train_reader,
event_handler=_event_handler,
feeding=feeding,
num_passes=num_passes)
logger.info("Training has finished.")
def __init__(self,
dnn_dims=[],
vocab_sizes=[],
model_type=ModelType.create_classification(),
model_arch=ModelArch.create_cnn(),
share_semantic_generator=False,
class_num=None,
share_embed=False,
is_infer=False):
'''
@dnn_dims: list of int
dimentions of each layer in semantic vector generator.
@vocab_sizes: 2-d tuple
size of both left and right items.
@model_type: int
type of task, should be 'rank: 0', 'regression: 1' or 'classification: 2'
@model_arch: int
model architecture
@share_semantic_generator: bool
whether to share the semantic vector generator for both left and right.
@share_embed: bool
whether to share the embeddings between left and right.
@class_num: int
number of categories.
'''
assert len(
vocab_sizes
) == 2, "vocab_sizes specify the sizes left and right inputs, and dim should be 2."
assert len(dnn_dims) > 1, "more than two layers is needed."
self.dnn_dims = dnn_dims
self.vocab_sizes = vocab_sizes
self.share_semantic_generator = share_semantic_generator
self.share_embed = share_embed
self.model_type = ModelType(model_type)
self.model_arch = ModelArch(model_arch)
self.class_num = class_num
self.is_infer = is_infer
logger.warning("build DSSM model with config of %s, %s" %
(self.model_type, self.model_arch))
logger.info("vocabulary sizes: %s" % str(self.vocab_sizes))
# bind model architecture
_model_arch = {
'cnn': self.create_cnn,
'fc': self.create_fc,
'rnn': self.create_rnn,
}
def _model_arch_creater(emb, prefix=''):
sent_vec = _model_arch.get(str(model_arch))(emb, prefix)
dnn = self.create_dnn(sent_vec, prefix)
return dnn
self.model_arch_creater = _model_arch_creater
# build model type
_model_type = {
'classification': self._build_classification_model,
'rank': self._build_rank_model,
'regression': self._build_regression_model,
}
print 'model type: ', str(self.model_type)
self.model_type_creater = _model_type[str(self.model_type)]
class DSSM(object):
def __init__(self,
dnn_dims=[],
vocab_sizes=[],
model_type=ModelType.create_classification(),
model_arch=ModelArch.create_cnn(),
share_semantic_generator=False,
class_num=None,
share_embed=False,
is_infer=False):
'''
@dnn_dims: list of int
dimentions of each layer in semantic vector generator.
@vocab_sizes: 2-d tuple
size of both left and right items.
@model_type: int
type of task, should be 'rank: 0', 'regression: 1' or 'classification: 2'
@model_arch: int
model architecture
@share_semantic_generator: bool
whether to share the semantic vector generator for both left and right.
@share_embed: bool
whether to share the embeddings between left and right.
@class_num: int
number of categories.
'''
assert len(
vocab_sizes
) == 2, "vocab_sizes specify the sizes left and right inputs, and dim should be 2."
assert len(dnn_dims) > 1, "more than two layers is needed."
self.dnn_dims = dnn_dims
self.vocab_sizes = vocab_sizes
self.share_semantic_generator = share_semantic_generator
self.share_embed = share_embed
self.model_type = ModelType(model_type)
self.model_arch = ModelArch(model_arch)
self.class_num = class_num
self.is_infer = is_infer
logger.warning("build DSSM model with config of %s, %s" %
(self.model_type, self.model_arch))
logger.info("vocabulary sizes: %s" % str(self.vocab_sizes))
# bind model architecture
_model_arch = {
'cnn': self.create_cnn,
'fc': self.create_fc,
'rnn': self.create_rnn,
}
def _model_arch_creater(emb, prefix=''):
sent_vec = _model_arch.get(str(model_arch))(emb, prefix)
dnn = self.create_dnn(sent_vec, prefix)
return dnn
self.model_arch_creater = _model_arch_creater
# build model type
_model_type = {
'classification': self._build_classification_model,
'rank': self._build_rank_model,
'regression': self._build_regression_model,
}
print 'model type: ', str(self.model_type)
self.model_type_creater = _model_type[str(self.model_type)]
def __call__(self):
return self.model_type_creater()
def create_embedding(self, input, prefix=''):
'''
Create an embedding table whose name has a `prefix`.
'''
logger.info("create embedding table [%s] which dimention is %d" %
(prefix, self.dnn_dims[0]))
emb = paddle.layer.embedding(
input=input,
size=self.dnn_dims[0],
param_attr=ParamAttr(name='%s_emb.w' % prefix))
return emb
def create_fc(self, emb, prefix=''):
'''
A multi-layer fully connected neural networks.
@emb: paddle.layer
output of the embedding layer
@prefix: str
prefix of layers' names, used to share parameters between more than one `fc` parts.
'''
_input_layer = paddle.layer.pooling(
input=emb, pooling_type=paddle.pooling.Max())
fc = paddle.layer.fc(input=_input_layer, size=self.dnn_dims[1])
return fc
def create_rnn(self, emb, prefix=''):
'''
A GRU sentence vector learner.
'''
gru = paddle.networks.simple_gru(input=emb, size=256)
sent_vec = paddle.layer.last_seq(gru)
return sent_vec
def create_cnn(self, emb, prefix=''):
'''
A multi-layer CNN.
@emb: paddle.layer
output of the embedding layer
@prefix: str
prefix of layers' names, used to share parameters between more than one `cnn` parts.
'''
def create_conv(context_len, hidden_size, prefix):
key = "%s_%d_%d" % (prefix, context_len, hidden_size)
conv = paddle.networks.sequence_conv_pool(
input=emb,
context_len=context_len,
hidden_size=hidden_size,
# set parameter attr for parameter sharing
context_proj_param_attr=ParamAttr(name=key + 'contex_proj.w'),
fc_param_attr=ParamAttr(name=key + '_fc.w'),
fc_bias_attr=ParamAttr(name=key + '_fc.b'),
pool_bias_attr=ParamAttr(name=key + '_pool.b'))
return conv
logger.info('create a sequence_conv_pool which context width is 3')
conv_3 = create_conv(3, self.dnn_dims[1], "cnn")
logger.info('create a sequence_conv_pool which context width is 4')
conv_4 = create_conv(4, self.dnn_dims[1], "cnn")
return conv_3, conv_4
def create_dnn(self, sent_vec, prefix):
# if more than three layers, than a fc layer will be added.
if len(self.dnn_dims) > 1:
_input_layer = sent_vec
for id, dim in enumerate(self.dnn_dims[1:]):
name = "%s_fc_%d_%d" % (prefix, id, dim)
logger.info("create fc layer [%s] which dimention is %d" %
(name, dim))
fc = paddle.layer.fc(
name=name,
input=_input_layer,
size=dim,
act=paddle.activation.Tanh(),
param_attr=ParamAttr(name='%s.w' % name),
bias_attr=ParamAttr(name='%s.b' % name))
_input_layer = fc
return _input_layer
def _build_classification_model(self):
logger.info("build classification model")
assert self.model_type.is_classification()
return self._build_classification_or_regression_model(
is_classification=True)
def _build_regression_model(self):
logger.info("build regression model")
assert self.model_type.is_regression()
return self._build_classification_or_regression_model(
is_classification=False)
def _build_rank_model(self):
'''
Build a pairwise rank model, and the cost is returned.
A pairwise rank model has 3 inputs:
- source sentence
- left_target sentence
- right_target sentence
- label, 1 if left_target should be sorted in front of right_target, otherwise 0.
'''
logger.info("build rank model")
assert self.model_type.is_rank()
source = paddle.layer.data(
name='source_input',
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[0]))
left_target = paddle.layer.data(
name='left_target_input',
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[1]))
right_target = paddle.layer.data(
name='right_target_input',
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[1]))
if not self.is_infer:
label = paddle.layer.data(
name='label_input', type=paddle.data_type.integer_value(1))
prefixs = '_ _ _'.split(
) if self.share_semantic_generator else 'source left right'.split()
embed_prefixs = '_ _'.split(
) if self.share_embed else 'source target target'.split()
word_vecs = []
for id, input in enumerate([source, left_target, right_target]):
x = self.create_embedding(input, prefix=embed_prefixs[id])
word_vecs.append(x)
semantics = []
for id, input in enumerate(word_vecs):
x = self.model_arch_creater(input, prefix=prefixs[id])
semantics.append(x)
# cossim score of source and left_target
left_score = paddle.layer.cos_sim(semantics[0], semantics[1])
# cossim score of source and right target
right_score = paddle.layer.cos_sim(semantics[0], semantics[2])
if not self.is_infer:
# rank cost
cost = paddle.layer.rank_cost(left_score, right_score, label=label)
# prediction = left_score - right_score
# but this operator is not supported currently.
# so AUC will not used.
return cost, None, label
return right_score
def _build_classification_or_regression_model(self, is_classification):
'''
Build a classification/regression model, and the cost is returned.
A Classification has 3 inputs:
- source sentence
- target sentence
- classification label
'''
if is_classification:
# prepare inputs.
assert self.class_num
source = paddle.layer.data(
name='source_input',
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[0]))
target = paddle.layer.data(
name='target_input',
type=paddle.data_type.integer_value_sequence(self.vocab_sizes[1]))
label = paddle.layer.data(
name='label_input',
type=paddle.data_type.integer_value(self.class_num)
if is_classification else paddle.data_type.dense_vector(1))
prefixs = '_ _'.split(
) if self.share_semantic_generator else 'left right'.split()
embed_prefixs = '_ _'.split(
) if self.share_embed else 'left right'.split()
word_vecs = []
for id, input in enumerate([source, target]):
x = self.create_embedding(input, prefix=embed_prefixs[id])
word_vecs.append(x)
semantics = []
for id, input in enumerate(word_vecs):
x = self.model_arch_creater(input, prefix=prefixs[id])
semantics.append(x)
if is_classification:
concated_vector = paddle.layer.concat(semantics)
prediction = paddle.layer.fc(
input=concated_vector,
size=self.class_num,
act=paddle.activation.Softmax())
cost = paddle.layer.classification_cost(
input=prediction, label=label)
else:
prediction = paddle.layer.cos_sim(*semantics)
cost = paddle.layer.square_error_cost(prediction, label)
if not self.is_infer:
return cost, prediction, label
return prediction
