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PaddleRec
未验证 提交 9b3afd7c 编写于 作者: W wuzhihua 提交者： GitHub
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Merge pull request #18 from 123malin/modify_yaml 

fix match/dssm
上级 730495a7 13bafd53
隐藏空白更改
内联 并排
Showing with 603 addition and 698 deletion
core/model.py
浏览文件 @ 9b3afd7c
......@@ -149,11 +149,13 @@ class Model(object):
return optimizer_i
def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
optimizer = envs.get_global_env("hyper_parameters.optimizer", None,
self._namespace)
return self._build_optimizer(optimizer, learning_rate)
opt_name = envs.get_global_env("hyper_parameters.optimizer.class")
opt_lr = envs.get_global_env(
"hyper_parameters.optimizer.learning_rate")
opt_strategy = envs.get_global_env(
"hyper_parameters.optimizer.strategy")
return self._build_optimizer(opt_name, opt_lr, opt_strategy)
def input_data(self, is_infer=False, **kwargs):
name = "dataset." + kwargs.get("dataset_name") + "."
......
core/trainers/single_infer.py
浏览文件 @ 9b3afd7c
......@@ -167,6 +167,7 @@ class SingleInfer(TranspileTrainer):
model = envs.lazy_instance_by_fliename(
model_path, "Model")(self._env)
model._infer_data_var = model.input_data(
is_infer=True,
dataset_name=model_dict["dataset_name"])
if envs.get_global_env("dataset." + dataset_name +
".type") == "DataLoader":
......
core/trainers/single_trainer.py
浏览文件 @ 9b3afd7c
......@@ -147,11 +147,6 @@ class SingleTrainer(TranspileTrainer):
startup_program = fluid.Program()
scope = fluid.Scope()
dataset_name = model_dict["dataset_name"]
opt_name = envs.get_global_env("hyper_parameters.optimizer.class")
opt_lr = envs.get_global_env(
"hyper_parameters.optimizer.learning_rate")
opt_strategy = envs.get_global_env(
"hyper_parameters.optimizer.strategy")
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
with fluid.scope_guard(scope):
......@@ -168,8 +163,7 @@ class SingleTrainer(TranspileTrainer):
self._get_dataloader(dataset_name,
model._data_loader)
model.net(model._data_var, False)
optimizer = model._build_optimizer(opt_name, opt_lr,
opt_strategy)
optimizer = model.optimizer()
optimizer.minimize(model._cost)
self._model[model_dict["name"]][0] = train_program
self._model[model_dict["name"]][1] = startup_program
......
models/match/dssm/config.yaml
浏览文件 @ 9b3afd7c
......@@ -11,44 +11,66 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
reader:
batch_size: 1
class: "{workspace}/synthetic_evaluate_reader.py"
test_data_path: "{workspace}/data/train"
train:
trainer:
# for cluster training
strategy: "async"
epochs: 4
workspace: "paddlerec.models.match.dssm"
reader:
batch_size: 4
class: "{workspace}/synthetic_reader.py"
train_data_path: "{workspace}/data/train"
workspace: "paddlerec.models.match.dssm"
dataset:
- name: dataset_train
batch_size: 4
type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/synthetic_reader.py"
- name: dataset_infer
batch_size: 1
type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/synthetic_evaluate_reader.py"
model:
models: "{workspace}/model.py"
hyper_parameters:
TRIGRAM_D: 1000
NEG: 4
fc_sizes: [300, 300, 128]
fc_acts: ['tanh', 'tanh', 'tanh']
learning_rate: 0.01
optimizer: sgd
hyper_parameters:
optimizer:
class: sgd
learning_rate: 0.01
strategy: async
trigram_d: 1000
neg_num: 4
fc_sizes: [300, 300, 128]
fc_acts: ['tanh', 'tanh', 'tanh']
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
mode: train_runner
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: train_runner
class: single_train
# num of epochs
epochs: 4
# device to run training or infer
device: cpu
save_checkpoint_interval: 2 # save model interval of epochs
save_inference_interval: 4 # save inference
save_checkpoint_path: "increment" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: ["query", "doc_pos"] # feed vars of save inference
save_inference_fetch_varnames: ["cos_sim_0.tmp_0"] # fetch vars of save inference
init_model_path: "" # load model path
fetch_period: 2
- name: infer_runner
class: single_infer
# num of epochs
epochs: 1
# device to run training or infer
device: cpu
fetch_period: 1
init_model_path: "increment/2" # load model path
inference:
dirname: "inference"
epoch_interval: 4
feed_varnames: ["query", "doc_pos"]
fetch_varnames: ["cos_sim_0.tmp_0"]
save_last: True
# runner will run all the phase in each epoch
phase:
- name: phase1
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_train # select dataset by name
thread_num: 1
#- name: phase2
# model: "{workspace}/model.py" # user-defined model
# dataset_name: dataset_infer # select dataset by name
# thread_num: 1
models/match/dssm/model.py
浏览文件 @ 9b3afd7c
......@@ -22,45 +22,39 @@ class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def input(self):
TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None,
self._namespace)
Neg = envs.get_global_env("hyper_parameters.NEG", None,
self._namespace)
self.query = fluid.data(
name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
self.doc_pos = fluid.data(
def _init_hyper_parameters(self):
self.trigram_d = envs.get_global_env("hyper_parameters.trigram_d")
self.neg_num = envs.get_global_env("hyper_parameters.neg_num")
self.hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes")
self.hidden_acts = envs.get_global_env("hyper_parameters.fc_acts")
self.learning_rate = envs.get_global_env(
"hyper_parameters.learning_rate")
def input_data(self, is_infer=False, **kwargs):
query = fluid.data(
name="query",
shape=[-1, self.trigram_d],
dtype='float32',
lod_level=0)
doc_pos = fluid.data(
name="doc_pos",
shape=[-1, TRIGRAM_D],
shape=[-1, self.trigram_d],
dtype='float32',
lod_level=0)
self.doc_negs = [
if is_infer:
return [query, doc_pos]
doc_negs = [
fluid.data(
name="doc_neg_" + str(i),
shape=[-1, TRIGRAM_D],
shape=[-1, self.trigram_d],
dtype="float32",
lod_level=0) for i in range(Neg)
lod_level=0) for i in range(self.neg_num)
]
self._data_var.append(self.query)
self._data_var.append(self.doc_pos)
for input in self.doc_negs:
self._data_var.append(input)
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
def net(self, is_infer=False):
hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None,
self._namespace)
hidden_acts = envs.get_global_env("hyper_parameters.fc_acts", None,
self._namespace)
return [query, doc_pos] + doc_negs
def net(self, inputs, is_infer=False):
def fc(data, hidden_layers, hidden_acts, names):
fc_inputs = [data]
for i in range(len(hidden_layers)):
......@@ -77,71 +71,30 @@ class Model(ModelBase):
fc_inputs.append(out)
return fc_inputs[-1]
query_fc = fc(self.query, hidden_layers, hidden_acts,
query_fc = fc(inputs[0], self.hidden_layers, self.hidden_acts,
['query_l1', 'query_l2', 'query_l3'])
doc_pos_fc = fc(self.doc_pos, hidden_layers, hidden_acts,
doc_pos_fc = fc(inputs[1], self.hidden_layers, self.hidden_acts,
['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3'])
self.R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
if is_infer:
self._infer_results["query_doc_sim"] = R_Q_D_p
return
R_Q_D_ns = []
for i, doc_neg in enumerate(self.doc_negs):
doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts, [
'doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i),
'doc_neg_l3_' + str(i)
])
for i in range(len(inputs) - 2):
doc_neg_fc_i = fc(
inputs[i + 2], self.hidden_layers, self.hidden_acts, [
'doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i),
'doc_neg_l3_' + str(i)
])
R_Q_D_ns.append(fluid.layers.cos_sim(query_fc, doc_neg_fc_i))
concat_Rs = fluid.layers.concat(
input=[self.R_Q_D_p] + R_Q_D_ns, axis=-1)
concat_Rs = fluid.layers.concat(input=[R_Q_D_p] + R_Q_D_ns, axis=-1)
prob = fluid.layers.softmax(concat_Rs, axis=1)
hit_prob = fluid.layers.slice(
prob, axes=[0, 1], starts=[0, 0], ends=[4, 1])
loss = -fluid.layers.reduce_sum(fluid.layers.log(hit_prob))
self.avg_cost = fluid.layers.mean(x=loss)
def infer_results(self):
self._infer_results['query_doc_sim'] = self.R_Q_D_p
def avg_loss(self):
self._cost = self.avg_cost
def metrics(self):
self._metrics["LOSS"] = self.avg_cost
def train_net(self):
self.input()
self.net(is_infer=False)
self.avg_loss()
self.metrics()
def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
optimizer = fluid.optimizer.SGD(learning_rate)
return optimizer
def infer_input(self):
TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None,
self._namespace)
self.query = fluid.data(
name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
self.doc_pos = fluid.data(
name="doc_pos",
shape=[-1, TRIGRAM_D],
dtype='float32',
lod_level=0)
self._infer_data_var = [self.query, self.doc_pos]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
def infer_net(self):
self.infer_input()
self.net(is_infer=True)
self.infer_results()
avg_cost = fluid.layers.mean(x=loss)
self._cost = avg_cost
self._metrics["LOSS"] = avg_cost
models/match/dssm/synthetic_evaluate_reader.py
浏览文件 @ 9b3afd7c
......@@ -16,7 +16,7 @@ from __future__ import print_function
from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
class TrainReader(Reader):
def init(self):
pass
......
models/match/multiview-simnet/config.yaml
浏览文件 @ 9b3afd7c
......@@ -11,49 +11,73 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
workspace: "paddlerec.models.match.multiview-simnet"
reader:
batch_size: 2
class: "{workspace}/evaluate_reader.py"
test_data_path: "{workspace}/data/test"
train:
trainer:
# for cluster training
strategy: "async"
# workspace
workspace: "paddlerec.models.match.multiview-simnet"
epochs: 2
workspace: "paddlerec.models.match.multiview-simnet"
# list of dataset
dataset:
- name: dataset_train # name of dataset to distinguish different datasets
batch_size: 2
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/train"
sparse_slots: "1 2 3"
- name: dataset_infer # name
batch_size: 2
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/test"
sparse_slots: "1 2"
reader:
batch_size: 2
class: "{workspace}/reader.py"
train_data_path: "{workspace}/data/train"
dataset_class: "DataLoader"
# hyper parameters of user-defined network
hyper_parameters:
optimizer:
class: Adam
learning_rate: 0.0001
strategy: async
query_encoder: "bow"
title_encoder: "bow"
query_encode_dim: 128
title_encode_dim: 128
sparse_feature_dim: 1000001
embedding_dim: 128
hidden_size: 128
margin: 0.1
model:
models: "{workspace}/model.py"
hyper_parameters:
use_DataLoader: True
query_encoder: "bow"
title_encoder: "bow"
query_encode_dim: 128
title_encode_dim: 128
query_slots: 1
title_slots: 1
sparse_feature_dim: 1000001
embedding_dim: 128
hidden_size: 128
learning_rate: 0.0001
optimizer: adam
# select runner by name
mode: train_runner
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: train_runner
class: single_train
# num of epochs
epochs: 2
# device to run training or infer
device: cpu
save_checkpoint_interval: 1 # save model interval of epochs
save_inference_interval: 1 # save inference
save_checkpoint_path: "increment" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
fetch_period: 1
- name: infer_runner
class: single_infer
# num of epochs
epochs: 1
# device to run training or infer
device: cpu
fetch_period: 1
init_model_path: "increment/0" # load model path
save:
increment:
dirname: "increment"
epoch_interval: 1
save_last: True
inference:
dirname: "inference"
epoch_interval: 1
save_last: True
# runner will run all the phase in each epoch
phase:
- name: phase1
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_train # select dataset by name
thread_num: 1
#- name: phase2
# model: "{workspace}/model.py" # user-defined model
# dataset_name: dataset_infer # select dataset by name
# thread_num: 1
models/match/multiview-simnet/model.py
浏览文件 @ 9b3afd7c
......@@ -99,143 +99,89 @@ class SimpleEncoderFactory(object):
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.init_config()
def init_config(self):
self._fetch_interval = 1
query_encoder = envs.get_global_env("hyper_parameters.query_encoder",
None, self._namespace)
title_encoder = envs.get_global_env("hyper_parameters.title_encoder",
None, self._namespace)
query_encode_dim = envs.get_global_env(
"hyper_parameters.query_encode_dim", None, self._namespace)
title_encode_dim = envs.get_global_env(
"hyper_parameters.title_encode_dim", None, self._namespace)
query_slots = envs.get_global_env("hyper_parameters.query_slots", None,
self._namespace)
title_slots = envs.get_global_env("hyper_parameters.title_slots", None,
self._namespace)
factory = SimpleEncoderFactory()
self.query_encoders = [
factory.create(query_encoder, query_encode_dim)
for i in range(query_slots)
]
self.title_encoders = [
factory.create(title_encoder, title_encode_dim)
for i in range(title_slots)
]
def _init_hyper_parameters(self):
self.query_encoder = envs.get_global_env(
"hyper_parameters.query_encoder")
self.title_encoder = envs.get_global_env(
"hyper_parameters.title_encoder")
self.query_encode_dim = envs.get_global_env(
"hyper_parameters.query_encode_dim")
self.title_encode_dim = envs.get_global_env(
"hyper_parameters.title_encode_dim")
self.emb_size = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None, self._namespace)
self.emb_dim = envs.get_global_env("hyper_parameters.embedding_dim",
None, self._namespace)
"hyper_parameters.sparse_feature_dim")
self.emb_dim = envs.get_global_env("hyper_parameters.embedding_dim")
self.emb_shape = [self.emb_size, self.emb_dim]
self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size",
None, self._namespace)
self.margin = 0.1
def input(self, is_train=True):
self.q_slots = [
fluid.data(
name="%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
for i in range(len(self.query_encoders))
]
self.pt_slots = [
fluid.data(
name="%d" % (i + len(self.query_encoders)),
shape=[None, 1],
lod_level=1,
dtype='int64') for i in range(len(self.title_encoders))
]
if is_train == False:
return self.q_slots + self.pt_slots
self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
self.margin = envs.get_global_env("hyper_parameters.margin")
self.nt_slots = [
fluid.data(
name="%d" %
(i + len(self.query_encoders) + len(self.title_encoders)),
shape=[None, 1],
lod_level=1,
dtype='int64') for i in range(len(self.title_encoders))
def net(self, input, is_infer=False):
factory = SimpleEncoderFactory()
self.q_slots = self._sparse_data_var[0:1]
self.query_encoders = [
factory.create(self.query_encoder, self.query_encode_dim)
for _ in self.q_slots
]
return self.q_slots + self.pt_slots + self.nt_slots
def train_input(self):
res = self.input()
self._data_var = res
use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader",
False, self._namespace)
if self._platform != "LINUX" or use_dataloader:
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=256,
use_double_buffer=False,
iterable=False)
def get_acc(self, x, y):
less = tensor.cast(cf.less_than(x, y), dtype='float32')
label_ones = fluid.layers.fill_constant_batch_size_like(
input=x, dtype='float32', shape=[-1, 1], value=1.0)
correct = fluid.layers.reduce_sum(less)
total = fluid.layers.reduce_sum(label_ones)
acc = fluid.layers.elementwise_div(correct, total)
return acc
def net(self):
q_embs = [
fluid.embedding(
input=query, size=self.emb_shape, param_attr="emb")
for query in self.q_slots
]
pt_embs = [
fluid.embedding(
input=title, size=self.emb_shape, param_attr="emb")
for title in self.pt_slots
]
nt_embs = [
fluid.embedding(
input=title, size=self.emb_shape, param_attr="emb")
for title in self.nt_slots
]
# encode each embedding field with encoder
q_encodes = [
self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
]
pt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(pt_embs)
]
nt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(nt_embs)
]
# concat multi view for query, pos_title, neg_title
q_concat = fluid.layers.concat(q_encodes)
pt_concat = fluid.layers.concat(pt_encodes)
nt_concat = fluid.layers.concat(nt_encodes)
# projection of hidden layer
q_hid = fluid.layers.fc(q_concat,
size=self.hidden_size,
param_attr='q_fc.w',
bias_attr='q_fc.b')
self.pt_slots = self._sparse_data_var[1:2]
self.title_encoders = [
factory.create(self.title_encoder, self.title_encode_dim)
]
pt_embs = [
fluid.embedding(
input=title, size=self.emb_shape, param_attr="emb")
for title in self.pt_slots
]
pt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(pt_embs)
]
pt_concat = fluid.layers.concat(pt_encodes)
pt_hid = fluid.layers.fc(pt_concat,
size=self.hidden_size,
param_attr='t_fc.w',
bias_attr='t_fc.b')
# cosine of hidden layers
cos_pos = fluid.layers.cos_sim(q_hid, pt_hid)
if is_infer:
self._infer_results['query_pt_sim'] = cos_pos
return
self.nt_slots = self._sparse_data_var[2:3]
nt_embs = [
fluid.embedding(
input=title, size=self.emb_shape, param_attr="emb")
for title in self.nt_slots
]
nt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(nt_embs)
]
nt_concat = fluid.layers.concat(nt_encodes)
nt_hid = fluid.layers.fc(nt_concat,
size=self.hidden_size,
param_attr='t_fc.w',
bias_attr='t_fc.b')
# cosine of hidden layers
cos_pos = fluid.layers.cos_sim(q_hid, pt_hid)
cos_neg = fluid.layers.cos_sim(q_hid, nt_hid)
# pairwise hinge_loss
......@@ -254,72 +200,16 @@ class Model(ModelBase):
input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_part2)
self.avg_cost = fluid.layers.mean(loss_part3)
self._cost = fluid.layers.mean(loss_part3)
self.acc = self.get_acc(cos_neg, cos_pos)
def avg_loss(self):
self._cost = self.avg_cost
def metrics(self):
self._metrics["loss"] = self.avg_cost
self._metrics["loss"] = self._cost
self._metrics["acc"] = self.acc
def train_net(self):
self.train_input()
self.net()
self.avg_loss()
self.metrics()
def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
return optimizer
def infer_input(self):
res = self.input(is_train=False)
self._infer_data_var = res
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
def infer_net(self):
self.infer_input()
# lookup embedding for each slot
q_embs = [
fluid.embedding(
input=query, size=self.emb_shape, param_attr="emb")
for query in self.q_slots
]
pt_embs = [
fluid.embedding(
input=title, size=self.emb_shape, param_attr="emb")
for title in self.pt_slots
]
# encode each embedding field with encoder
q_encodes = [
self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
]
pt_encodes = [
self.title_encoders[i].forward(emb)
for i, emb in enumerate(pt_embs)
]
# concat multi view for query, pos_title, neg_title
q_concat = fluid.layers.concat(q_encodes)
pt_concat = fluid.layers.concat(pt_encodes)
# projection of hidden layer
q_hid = fluid.layers.fc(q_concat,
size=self.hidden_size,
param_attr='q_fc.w',
bias_attr='q_fc.b')
pt_hid = fluid.layers.fc(pt_concat,
size=self.hidden_size,
param_attr='t_fc.w',
bias_attr='t_fc.b')
# cosine of hidden layers
cos = fluid.layers.cos_sim(q_hid, pt_hid)
self._infer_results['query_pt_sim'] = cos
def get_acc(self, x, y):
less = tensor.cast(cf.less_than(x, y), dtype='float32')
label_ones = fluid.layers.fill_constant_batch_size_like(
input=x, dtype='float32', shape=[-1, 1], value=1.0)
correct = fluid.layers.reduce_sum(less)
total = fluid.layers.reduce_sum(label_ones)
acc = fluid.layers.elementwise_div(correct, total)
return acc
models/match/readme.md
浏览文件 @ 9b3afd7c
......@@ -31,9 +31,21 @@
<img align="center" src="../../doc/imgs/multiview-simnet.png">
<p>
## 使用教程
### 训练&预测
## 使用教程(快速开始)
### 训练
```shell
python -m paddlerec.run -m paddlerec.models.match.dssm # dssm
python -m paddlerec.run -m paddlerec.models.match.multiview-simnet # multiview-simnet
```
### 预测
```shell
# 修改对应模型的config.yaml, workspace配置为当前目录的绝对路径
# 修改对应模型的config.yaml，mode配置infer_runner
# 示例: mode: train_runner -> mode: infer_runner
# infer_runner中 class配置为 class: single_infer
# 修改phase阶段为infer的配置，参照config注释
# 修改完config.yaml后 执行:
python -m paddlerec.run -m ./config.yaml # 以dssm为例
```
models/recall/gnn/config.yaml
浏览文件 @ 9b3afd7c
......@@ -11,46 +11,71 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
workspace: "paddlerec.models.recall.gnn"
reader:
batch_size: 50
class: "{workspace}/evaluate_reader.py"
test_data_path: "{workspace}/data/test"
train:
trainer:
# for cluster training
strategy: "async"
# workspace
workspace: "paddlerec.models.recall.gnn"
epochs: 2
workspace: "paddlerec.models.recall.gnn"
# list of dataset
dataset:
- name: dataset_train # name of dataset to distinguish different datasets
batch_size: 100
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/reader.py"
- name: dataset_infer # name
batch_size: 50
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/test"
data_converter: "{workspace}/evaluate_reader.py"
reader:
batch_size: 100
class: "{workspace}/reader.py"
train_data_path: "{workspace}/data/train"
dataset_class: "DataLoader"
# hyper parameters of user-defined network
hyper_parameters:
optimizer:
class: Adam
learning_rate: 0.001
decay_steps: 3
decay_rate: 0.1
l2: 0.00001
sparse_feature_number: 43098
sparse_feature_dim: 100
corpus_size: 719470
gnn_propogation_steps: 1
model:
models: "{workspace}/model.py"
hyper_parameters:
use_DataLoader: True
config_path: "{workspace}/data/config.txt"
sparse_feature_dim: 100
gnn_propogation_steps: 1
learning_rate: 0.001
l2: 0.00001
decay_steps: 3
decay_rate: 0.1
optimizer: adam
# select runner by name
mode: train_runner
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: train_runner
class: single_train
# num of epochs
epochs: 2
# device to run training or infer
device: cpu
save_checkpoint_interval: 1 # save model interval of epochs
save_inference_interval: 1 # save inference
save_checkpoint_path: "increment" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
fetch_period: 10
- name: infer_runner
class: single_infer
# num of epochs
epochs: 1
# device to run training or infer
device: cpu
fetch_period: 1
init_model_path: "increment/0" # load model path
save:
increment:
dirname: "increment"
epoch_interval: 1
save_last: True
inference:
dirname: "inference"
epoch_interval: 1
save_last: True
# runner will run all the phase in each epoch
phase:
- name: phase1
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_train # select dataset by name
thread_num: 1
#- name: phase2
# model: "{workspace}/model.py" # user-defined model
# dataset_name: dataset_infer # select dataset by name
# thread_num: 1
models/recall/gnn/data_process.sh → models/recall/gnn/data_prepare.sh
浏览文件 @ 9b3afd7c
......@@ -17,7 +17,7 @@
set -e
echo "begin to download data"
cd raw_data && python download.py
cd data && python download.py
mkdir diginetica
python preprocess.py --dataset diginetica
......@@ -26,8 +26,10 @@ python convert_data.py --data_dir diginetica
cat diginetica/train.txt | wc -l >> diginetica/config.txt
mkdir train_data
mv diginetica/train.txt train_data
rm -rf train && mkdir train
mv diginetica/train.txt train
mkdir test_data
mv diginetica/test.txt test_data
rm -rf test && mkdir test
mv diginetica/test.txt test
mv diginetica/config.txt ./config.txt
models/recall/gnn/evaluate_reader.py
浏览文件 @ 9b3afd7c
......@@ -21,10 +21,10 @@ from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class EvaluateReader(Reader):
class TrainReader(Reader):
def init(self):
self.batch_size = envs.get_global_env("batch_size", None,
"evaluate.reader")
self.batch_size = envs.get_global_env(
"dataset.dataset_infer.batch_size")
self.input = []
self.length = None
......
models/recall/gnn/model.py
浏览文件 @ 9b3afd7c
......@@ -25,74 +25,65 @@ from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.init_config()
def init_config(self):
self._fetch_interval = 1
self.items_num, self.ins_num = self.config_read(
envs.get_global_env("hyper_parameters.config_path", None,
self._namespace))
self.train_batch_size = envs.get_global_env("batch_size", None,
"train.reader")
self.evaluate_batch_size = envs.get_global_env("batch_size", None,
"evaluate.reader")
self.hidden_size = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None, self._namespace)
self.step = envs.get_global_env(
"hyper_parameters.gnn_propogation_steps", None, self._namespace)
def _init_hyper_parameters(self):
self.learning_rate = envs.get_global_env(
"hyper_parameters.optimizer.learning_rate")
self.decay_steps = envs.get_global_env(
"hyper_parameters.optimizer.decay_steps")
self.decay_rate = envs.get_global_env(
"hyper_parameters.optimizer.decay_rate")
self.l2 = envs.get_global_env("hyper_parameters.optimizer.l2")
self.dict_size = envs.get_global_env(
"hyper_parameters.sparse_feature_number")
self.corpus_size = envs.get_global_env("hyper_parameters.corpus_size")
def config_read(self, config_path=None):
if config_path is None:
raise ValueError(
"please set train.model.hyper_parameters.config_path at first")
with open(config_path, "r") as fin:
item_nums = int(fin.readline().strip())
ins_nums = int(fin.readline().strip())
return item_nums, ins_nums
self.train_batch_size = envs.get_global_env(
"dataset.dataset_train.batch_size")
self.evaluate_batch_size = envs.get_global_env(
"dataset.dataset_infer.batch_size")
def input(self, bs):
self.items = fluid.data(
self.hidden_size = envs.get_global_env(
"hyper_parameters.sparse_feature_dim")
self.step = envs.get_global_env(
"hyper_parameters.gnn_propogation_steps")
def input_data(self, is_infer=False, **kwargs):
if is_infer:
bs = self.evaluate_batch_size
else:
bs = self.train_batch_size
items = fluid.data(
name="items", shape=[bs, -1],
dtype="int64") # [batch_size, uniq_max]
self.seq_index = fluid.data(
seq_index = fluid.data(
name="seq_index", shape=[bs, -1, 2],
dtype="int32") # [batch_size, seq_max, 2]
self.last_index = fluid.data(
last_index = fluid.data(
name="last_index", shape=[bs, 2], dtype="int32") # [batch_size, 2]
self.adj_in = fluid.data(
adj_in = fluid.data(
name="adj_in", shape=[bs, -1, -1],
dtype="float32") # [batch_size, seq_max, seq_max]
self.adj_out = fluid.data(
adj_out = fluid.data(
name="adj_out", shape=[bs, -1, -1],
dtype="float32") # [batch_size, seq_max, seq_max]
self.mask = fluid.data(
mask = fluid.data(
name="mask", shape=[bs, -1, 1],
dtype="float32") # [batch_size, seq_max, 1]
self.label = fluid.data(
label = fluid.data(
name="label", shape=[bs, 1], dtype="int64") # [batch_size, 1]
res = [
self.items, self.seq_index, self.last_index, self.adj_in,
self.adj_out, self.mask, self.label
]
res = [items, seq_index, last_index, adj_in, adj_out, mask, label]
return res
def train_input(self):
res = self.input(self.train_batch_size)
self._data_var = res
use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader",
False, self._namespace)
def net(self, inputs, is_infer=False):
if is_infer:
bs = self.evaluate_batch_size
else:
bs = self.train_batch_size
if self._platform != "LINUX" or use_dataloader:
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=256,
use_double_buffer=False,
iterable=False)
def net(self, items_num, hidden_size, step, bs):
stdv = 1.0 / math.sqrt(hidden_size)
stdv = 1.0 / math.sqrt(self.hidden_size)
def embedding_layer(input,
table_name,
......@@ -100,22 +91,22 @@ class Model(ModelBase):
initializer_instance=None):
emb = fluid.embedding(
input=input,
size=[items_num, emb_dim],
size=[self.dict_size, emb_dim],
param_attr=fluid.ParamAttr(
name=table_name, initializer=initializer_instance), )
name=table_name, initializer=initializer_instance))
return emb
sparse_initializer = fluid.initializer.Uniform(low=-stdv, high=stdv)
items_emb = embedding_layer(self.items, "emb", hidden_size,
items_emb = embedding_layer(inputs[0], "emb", self.hidden_size,
sparse_initializer)
pre_state = items_emb
for i in range(step):
for i in range(self.step):
pre_state = layers.reshape(
x=pre_state, shape=[bs, -1, hidden_size])
x=pre_state, shape=[bs, -1, self.hidden_size])
state_in = layers.fc(
input=pre_state,
name="state_in",
size=hidden_size,
size=self.hidden_size,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
......@@ -127,7 +118,7 @@ class Model(ModelBase):
state_out = layers.fc(
input=pre_state,
name="state_out",
size=hidden_size,
size=self.hidden_size,
act=None,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
......@@ -137,33 +128,34 @@ class Model(ModelBase):
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [batch_size, uniq_max, h]
state_adj_in = layers.matmul(self.adj_in,
state_adj_in = layers.matmul(inputs[3],
state_in) # [batch_size, uniq_max, h]
state_adj_out = layers.matmul(
self.adj_out, state_out) # [batch_size, uniq_max, h]
inputs[4], state_out) # [batch_size, uniq_max, h]
gru_input = layers.concat([state_adj_in, state_adj_out], axis=2)
gru_input = layers.reshape(
x=gru_input, shape=[-1, hidden_size * 2])
x=gru_input, shape=[-1, self.hidden_size * 2])
gru_fc = layers.fc(input=gru_input,
name="gru_fc",
size=3 * hidden_size,
size=3 * self.hidden_size,
bias_attr=False)
pre_state, _, _ = fluid.layers.gru_unit(
input=gru_fc,
hidden=layers.reshape(
x=pre_state, shape=[-1, hidden_size]),
size=3 * hidden_size)
x=pre_state, shape=[-1, self.hidden_size]),
size=3 * self.hidden_size)
final_state = layers.reshape(pre_state, shape=[bs, -1, hidden_size])
seq = layers.gather_nd(final_state, self.seq_index)
last = layers.gather_nd(final_state, self.last_index)
final_state = layers.reshape(
pre_state, shape=[bs, -1, self.hidden_size])
seq = layers.gather_nd(final_state, inputs[1])
last = layers.gather_nd(final_state, inputs[2])
seq_fc = layers.fc(
input=seq,
name="seq_fc",
size=hidden_size,
size=self.hidden_size,
bias_attr=False,
act=None,
num_flatten_dims=2,
......@@ -171,7 +163,7 @@ class Model(ModelBase):
low=-stdv, high=stdv))) # [batch_size, seq_max, h]
last_fc = layers.fc(input=last,
name="last_fc",
size=hidden_size,
size=self.hidden_size,
bias_attr=False,
act=None,
num_flatten_dims=1,
......@@ -184,7 +176,7 @@ class Model(ModelBase):
add = layers.elementwise_add(seq_fc_t,
last_fc) # [seq_max, batch_size, h]
b = layers.create_parameter(
shape=[hidden_size],
shape=[self.hidden_size],
dtype='float32',
default_initializer=fluid.initializer.Constant(value=0.0)) # [h]
add = layers.elementwise_add(add, b) # [seq_max, batch_size, h]
......@@ -202,7 +194,7 @@ class Model(ModelBase):
bias_attr=False,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv))) # [batch_size, seq_max, 1]
weight *= self.mask
weight *= inputs[5]
weight_mask = layers.elementwise_mul(
seq, weight, axis=0) # [batch_size, seq_max, h]
global_attention = layers.reduce_sum(
......@@ -213,7 +205,7 @@ class Model(ModelBase):
final_attention_fc = layers.fc(
input=final_attention,
name="final_attention_fc",
size=hidden_size,
size=self.hidden_size,
bias_attr=False,
act=None,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
......@@ -225,7 +217,7 @@ class Model(ModelBase):
# dtype="int64",
# persistable=True,
# name="all_vocab")
all_vocab = np.arange(1, items_num).reshape((-1)).astype('int32')
all_vocab = np.arange(1, self.dict_size).reshape((-1)).astype('int32')
all_vocab = fluid.layers.cast(
x=fluid.layers.assign(all_vocab), dtype='int64')
......@@ -235,63 +227,32 @@ class Model(ModelBase):
name="emb",
initializer=fluid.initializer.Uniform(
low=-stdv, high=stdv)),
size=[items_num, hidden_size]) # [all_vocab, h]
size=[self.dict_size, self.hidden_size]) # [all_vocab, h]
logits = layers.matmul(
x=final_attention_fc, y=all_emb,
transpose_y=True) # [batch_size, all_vocab]
softmax = layers.softmax_with_cross_entropy(
logits=logits, label=self.label) # [batch_size, 1]
logits=logits, label=inputs[6]) # [batch_size, 1]
self.loss = layers.reduce_mean(softmax) # [1]
self.acc = layers.accuracy(input=logits, label=self.label, k=20)
self.acc = layers.accuracy(input=logits, label=inputs[6], k=20)
def avg_loss(self):
self._cost = self.loss
if is_infer:
self._infer_results['acc'] = self.acc
self._infer_results['loss'] = self.loss
return
def metrics(self):
self._metrics["LOSS"] = self.loss
self._metrics["train_acc"] = self.acc
def train_net(self):
self.train_input()
self.net(self.items_num, self.hidden_size, self.step,
self.train_batch_size)
self.avg_loss()
self.metrics()
def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
step_per_epoch = self.ins_num // self.train_batch_size
decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None,
self._namespace)
decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None,
self._namespace)
l2 = envs.get_global_env("hyper_parameters.l2", None, self._namespace)
step_per_epoch = self.corpus_size // self.train_batch_size
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.exponential_decay(
learning_rate=learning_rate,
decay_steps=decay_steps * step_per_epoch,
decay_rate=decay_rate),
learning_rate=self.learning_rate,
decay_steps=self.decay_steps * step_per_epoch,
decay_rate=self.decay_rate),
regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=l2))
regularization_coeff=self.l2))
return optimizer
def infer_input(self):
self._reader_namespace = "evaluate.reader"
res = self.input(self.evaluate_batch_size)
self._infer_data_var = res
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
def infer_net(self):
self.infer_input()
self.net(self.items_num, self.hidden_size, self.step,
self.evaluate_batch_size)
self._infer_results['acc'] = self.acc
self._infer_results['loss'] = self.loss
models/recall/gnn/reader.py
浏览文件 @ 9b3afd7c
......@@ -23,9 +23,8 @@ from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
self.batch_size = envs.get_global_env("batch_size", None,
"train.reader")
self.batch_size = envs.get_global_env(
"dataset.dataset_train.batch_size")
self.input = []
self.length = None
......
models/recall/readme.md
浏览文件 @ 9b3afd7c
......@@ -57,8 +57,8 @@
<img align="center" src="../../doc/imgs/gnn.png">
<p>
## 使用教程
### 训练 预测
## 使用教程(快速开始)
###
```shell
python -m paddlerec.run -m paddlerec.models.recall.word2vec # word2vec
python -m paddlerec.run -m paddlerec.models.recall.ssr # ssr
......@@ -67,6 +67,40 @@ python -m paddlerec.run -m paddlerec.models.recall.gnn # gnn
python -m paddlerec.run -m paddlerec.models.recall.ncf # ncf
python -m paddlerec.run -m paddlerec.models.recall.youtube_dnn # youtube_dnn
```
## 使用教程（复现论文）
为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。
| 模型 | batch_size | thread_num | epoch_num |
| :---: | :---: | :---: | :---: |
| Word2Vec | 100 | 5 | 5 |
| GNN | 100 | 1 | 30 |
| GRU4REC | 500 | 1 | 10 |
### 数据处理
参考每个模型目录数据下载&预处理脚本。
```bash
sh data_prepare.sh
```
### 训练
```bash
cd modles/recall/gnn # 进入选定好的召回模型的目录 以gnn为例
python -m paddlerec.run -m ./config.yaml # 自定义修改超参后，指定配置文件，使用自定义配置
```
### 预测
```
# 修改对应模型的config.yaml, workspace配置为当前目录的绝对路径
# 修改对应模型的config.yaml，mode配置infer_runner
# 示例: mode: train_runner -> mode: infer_runner
# infer_runner中 class配置为 class: single_infer
# 修改phase阶段为infer的配置，参照config注释
# 修改完config.yaml后 执行:
python -m paddlerec.run -m ./config.yaml # 以gnn为例
```
## 效果对比
### 模型效果列表
......
models/recall/word2vec/config.yaml
浏览文件 @ 9b3afd7c
......@@ -11,51 +11,70 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
workspace: "paddlerec.models.recall.word2vec"
workspace: "paddlerec.models.recall.word2vec"
evaluate_only: False
evaluate_model_path: ""
reader:
batch_size: 50
class: "{workspace}/w2v_evaluate_reader.py"
test_data_path: "{workspace}/data/test"
word_id_dict_path: "{workspace}/data/dict/word_id_dict.txt"
# list of dataset
dataset:
- name: dataset_train # name of dataset to distinguish different datasets
batch_size: 100
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/train"
word_count_dict_path: "{workspace}/data/dict/word_count_dict.txt"
data_converter: "{workspace}/w2v_reader.py"
- name: dataset_infer # name
batch_size: 50
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/test"
word_id_dict_path: "{workspace}/data/dict/word_id_dict.txt"
data_converter: "{workspace}/w2v_evaluate_reader.py"
train:
trainer:
# for cluster training
strategy: "async"
hyper_parameters:
optimizer:
learning_rate: 1.0
decay_steps: 100000
decay_rate: 0.999
class: sgd
strategy: async
sparse_feature_number: 354051
sparse_feature_dim: 300
with_shuffle_batch: False
neg_num: 5
window_size: 5
# select runner by name
mode: train_runner
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: train_runner
class: single_train
# num of epochs
epochs: 2
workspace: "paddlerec.models.recall.word2vec"
# device to run training or infer
device: cpu
save_checkpoint_interval: 1 # save model interval of epochs
save_inference_interval: 1 # save inference
save_checkpoint_path: "increment" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
fetch_period: 10
- name: infer_runner
class: single_infer
# num of epochs
epochs: 1
# device to run training or infer
device: cpu
init_model_path: "increment/0" # load model path
reader:
batch_size: 100
class: "{workspace}/w2v_reader.py"
train_data_path: "{workspace}/data/train"
word_count_dict_path: "{workspace}/data/dict/word_count_dict.txt"
model:
models: "{workspace}/model.py"
hyper_parameters:
sparse_feature_number: 85
sparse_feature_dim: 300
with_shuffle_batch: False
neg_num: 5
window_size: 5
learning_rate: 1.0
decay_steps: 100000
decay_rate: 0.999
optimizer: sgd
save:
increment:
dirname: "increment"
epoch_interval: 1
save_last: True
inference:
dirname: "inference"
epoch_interval: 1
save_last: True
# runner will run all the phase in each epoch
phase:
- name: phase1
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_train # select dataset by name
thread_num: 1
#- name: phase2
# model: "{workspace}/model.py" # user-defined model
# dataset_name: dataset_infer # select dataset by name
# thread_num: 1
models/recall/word2vec/prepare_data.sh → models/recall/word2vec/data_prepare.sh
浏览文件 @ 9b3afd7c
......@@ -22,16 +22,17 @@ tar xvf 1-billion-word-language-modeling-benchmark-r13output.tar
mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ raw_data/
# preprocess data
python preprocess.py --build_dict --build_dict_corpus_dir raw_data/training-monolingual.tokenized.shuffled --dict_path raw_data/test_build_dict
python preprocess.py --filter_corpus --dict_path raw_data/test_build_dict --input_corpus_dir raw_data/training-monolingual.tokenized.shuffled --output_corpus_dir raw_data/convert_text8 --min_count 5 --downsample 0.001
mkdir thirdparty
mv raw_data/test_build_dict thirdparty/
mv raw_data/test_build_dict_word_to_id_ thirdparty/
python preprocess.py --build_dict --build_dict_corpus_dir raw_data/training-monolingual.tokenized.shuffled --dict_path raw_data/word_count_dict.txt
python preprocess.py --filter_corpus --dict_path raw_data/word_count_dict.txt --input_corpus_dir raw_data/training-monolingual.tokenized.shuffled --output_corpus_dir raw_data/convert_text8 --min_count 5 --downsample 0.001
mv raw_data/word_count_dict.txt data/dict/
mv raw_data/word_id_dict.txt data/dict/
python preprocess.py --data_resplit --input_corpus_dir=raw_data/convert_text8 --output_corpus_dir=train_data
rm -rf data/train/*
rm -rf data/test/*
python preprocess.py --data_resplit --input_corpus_dir=raw_data/convert_text8 --output_corpus_dir=data/train
# download test data
wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
tar xzvf test_dir.tar -C raw_data
mv raw_data/data/test_dir test_data/
mv raw_data/data/test_dir/* data/test/
rm -rf raw_data
models/recall/word2vec/model.py
浏览文件 @ 9b3afd7c
......@@ -23,45 +23,50 @@ class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def input(self):
neg_num = int(
envs.get_global_env("hyper_parameters.neg_num", None,
self._namespace))
self.input_word = fluid.data(
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_trainer(
) == "CtrTrainer" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number")
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim")
self.neg_num = envs.get_global_env("hyper_parameters.neg_num")
self.with_shuffle_batch = envs.get_global_env(
"hyper_parameters.with_shuffle_batch")
self.learning_rate = envs.get_global_env(
"hyper_parameters.optimizer.learning_rate")
self.decay_steps = envs.get_global_env(
"hyper_parameters.optimizer.decay_steps")
self.decay_rate = envs.get_global_env(
"hyper_parameters.optimizer.decay_rate")
def input_data(self, is_infer=False, **kwargs):
if is_infer:
analogy_a = fluid.data(
name="analogy_a", shape=[None], dtype='int64')
analogy_b = fluid.data(
name="analogy_b", shape=[None], dtype='int64')
analogy_c = fluid.data(
name="analogy_c", shape=[None], dtype='int64')
analogy_d = fluid.data(
name="analogy_d", shape=[None], dtype='int64')
return [analogy_a, analogy_b, analogy_c, analogy_d]
input_word = fluid.data(
name="input_word", shape=[None, 1], dtype='int64')
self.true_word = fluid.data(
true_word = fluid.data(
name='true_label', shape=[None, 1], dtype='int64')
self._data_var.append(self.input_word)
self._data_var.append(self.true_word)
with_shuffle_batch = bool(
int(
envs.get_global_env("hyper_parameters.with_shuffle_batch",
None, self._namespace)))
if not with_shuffle_batch:
self.neg_word = fluid.data(
name="neg_label", shape=[None, neg_num], dtype='int64')
self._data_var.append(self.neg_word)
if self.with_shuffle_batch:
return [input_word, true_word]
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
neg_word = fluid.data(
name="neg_label", shape=[None, self.neg_num], dtype='int64')
return [input_word, true_word, neg_word]
def net(self):
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
neg_num = int(
envs.get_global_env("hyper_parameters.neg_num", None,
self._namespace))
sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None, self._namespace)
sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None, self._namespace)
with_shuffle_batch = bool(
int(
envs.get_global_env("hyper_parameters.with_shuffle_batch",
None, self._namespace)))
def net(self, inputs, is_infer=False):
if is_infer:
self.infer_net(inputs)
return
def embedding_layer(input,
table_name,
......@@ -71,8 +76,8 @@ class Model(ModelBase):
emb = fluid.embedding(
input=input,
is_sparse=True,
is_distributed=is_distributed,
size=[sparse_feature_number, emb_dim],
is_distributed=self.is_distributed,
size=[self.sparse_feature_number, emb_dim],
param_attr=fluid.ParamAttr(
name=table_name, initializer=initializer_instance), )
if squeeze:
......@@ -80,44 +85,44 @@ class Model(ModelBase):
else:
return emb
init_width = 0.5 / sparse_feature_dim
init_width = 0.5 / self.sparse_feature_dim
emb_initializer = fluid.initializer.Uniform(-init_width, init_width)
emb_w_initializer = fluid.initializer.Constant(value=0.0)
input_emb = embedding_layer(self.input_word, "emb", sparse_feature_dim,
input_emb = embedding_layer(inputs[0], "emb", self.sparse_feature_dim,
emb_initializer, True)
true_emb_w = embedding_layer(self.true_word, "emb_w",
sparse_feature_dim, emb_w_initializer,
True)
true_emb_b = embedding_layer(self.true_word, "emb_b", 1,
true_emb_w = embedding_layer(inputs[1], "emb_w",
self.sparse_feature_dim,
emb_w_initializer, True)
true_emb_b = embedding_layer(inputs[1], "emb_b", 1, emb_w_initializer,
True)
if with_shuffle_batch:
if self.with_shuffle_batch:
neg_emb_w_list = []
for i in range(neg_num):
for i in range(self.neg_num):
neg_emb_w_list.append(
fluid.contrib.layers.shuffle_batch(
true_emb_w)) # shuffle true_word
neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0)
neg_emb_w = fluid.layers.reshape(
neg_emb_w_concat, shape=[-1, neg_num, sparse_feature_dim])
neg_emb_w_concat,
shape=[-1, self.neg_num, self.sparse_feature_dim])
neg_emb_b_list = []
for i in range(neg_num):
for i in range(self.neg_num):
neg_emb_b_list.append(
fluid.contrib.layers.shuffle_batch(
true_emb_b)) # shuffle true_word
neg_emb_b = fluid.layers.concat(neg_emb_b_list, axis=0)
neg_emb_b_vec = fluid.layers.reshape(
neg_emb_b, shape=[-1, neg_num])
neg_emb_b, shape=[-1, self.neg_num])
else:
neg_emb_w = embedding_layer(self.neg_word, "emb_w",
sparse_feature_dim, emb_w_initializer)
neg_emb_b = embedding_layer(self.neg_word, "emb_b", 1,
neg_emb_w = embedding_layer(
inputs[2], "emb_w", self.sparse_feature_dim, emb_w_initializer)
neg_emb_b = embedding_layer(inputs[2], "emb_b", 1,
emb_w_initializer)
neg_emb_b_vec = fluid.layers.reshape(
neg_emb_b, shape=[-1, neg_num])
neg_emb_b, shape=[-1, self.neg_num])
true_logits = fluid.layers.elementwise_add(
fluid.layers.reduce_sum(
......@@ -127,18 +132,22 @@ class Model(ModelBase):
true_emb_b)
input_emb_re = fluid.layers.reshape(
input_emb, shape=[-1, 1, sparse_feature_dim])
input_emb, shape=[-1, 1, self.sparse_feature_dim])
neg_matmul = fluid.layers.matmul(
input_emb_re, neg_emb_w, transpose_y=True)
neg_logits = fluid.layers.elementwise_add(
fluid.layers.reshape(
neg_matmul, shape=[-1, neg_num]),
neg_emb_b_vec)
label_ones = fluid.layers.fill_constant_batch_size_like(
true_logits, shape=[-1, 1], value=1.0, dtype='float32')
label_zeros = fluid.layers.fill_constant_batch_size_like(
true_logits, shape=[-1, neg_num], value=0.0, dtype='float32')
neg_matmul_re = fluid.layers.reshape(
neg_matmul, shape=[-1, self.neg_num])
neg_logits = fluid.layers.elementwise_add(neg_matmul_re, neg_emb_b_vec)
#nce loss
label_ones = fluid.layers.fill_constant(
shape=[fluid.layers.shape(true_logits)[0], 1],
value=1.0,
dtype='float32')
label_zeros = fluid.layers.fill_constant(
shape=[fluid.layers.shape(true_logits)[0], self.neg_num],
value=0.0,
dtype='float32')
true_xent = fluid.layers.sigmoid_cross_entropy_with_logits(true_logits,
label_ones)
......@@ -149,7 +158,9 @@ class Model(ModelBase):
true_xent, dim=1),
fluid.layers.reduce_sum(
neg_xent, dim=1))
self.avg_cost = fluid.layers.reduce_mean(cost)
avg_cost = fluid.layers.reduce_mean(cost)
self._cost = avg_cost
global_right_cnt = fluid.layers.create_global_var(
name="global_right_cnt",
persistable=True,
......@@ -164,77 +175,33 @@ class Model(ModelBase):
value=0)
global_right_cnt.stop_gradient = True
global_total_cnt.stop_gradient = True
def avg_loss(self):
self._cost = self.avg_cost
def metrics(self):
self._metrics["LOSS"] = self.avg_cost
def train_net(self):
self.input()
self.net()
self.avg_loss()
self.metrics()
self._metrics["LOSS"] = avg_cost
def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None,
self._namespace)
decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None,
self._namespace)
optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=learning_rate,
decay_steps=decay_steps,
decay_rate=decay_rate,
learning_rate=self.learning_rate,
decay_steps=self.decay_steps,
decay_rate=self.decay_rate,
staircase=True))
return optimizer
def analogy_input(self):
sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None, self._namespace)
self.analogy_a = fluid.data(
name="analogy_a", shape=[None], dtype='int64')
self.analogy_b = fluid.data(
name="analogy_b", shape=[None], dtype='int64')
self.analogy_c = fluid.data(
name="analogy_c", shape=[None], dtype='int64')
self.analogy_d = fluid.data(
name="analogy_d", shape=[None], dtype='int64')
self._infer_data_var = [
self.analogy_a, self.analogy_b, self.analogy_c, self.analogy_d
]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
def infer_net(self):
sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None, self._namespace)
sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None, self._namespace)
def infer_net(self, inputs):
def embedding_layer(input, table_name, initializer_instance=None):
emb = fluid.embedding(
input=input,
size=[sparse_feature_number, sparse_feature_dim],
size=[self.sparse_feature_number, self.sparse_feature_dim],
param_attr=table_name)
return emb
self.analogy_input()
all_label = np.arange(sparse_feature_number).reshape(
sparse_feature_number).astype('int32')
all_label = np.arange(self.sparse_feature_number).reshape(
self.sparse_feature_number).astype('int32')
self.all_label = fluid.layers.cast(
x=fluid.layers.assign(all_label), dtype='int64')
emb_all_label = embedding_layer(self.all_label, "emb")
emb_a = embedding_layer(self.analogy_a, "emb")
emb_b = embedding_layer(self.analogy_b, "emb")
emb_c = embedding_layer(self.analogy_c, "emb")
emb_a = embedding_layer(inputs[0], "emb")
emb_b = embedding_layer(inputs[1], "emb")
emb_c = embedding_layer(inputs[2], "emb")
target = fluid.layers.elementwise_add(
fluid.layers.elementwise_sub(emb_b, emb_a), emb_c)
......@@ -245,8 +212,7 @@ class Model(ModelBase):
values, pred_idx = fluid.layers.topk(input=dist, k=4)
label = fluid.layers.expand(
fluid.layers.unsqueeze(
self.analogy_d, axes=[1]),
expand_times=[1, 4])
inputs[3], axes=[1]), expand_times=[1, 4])
label_ones = fluid.layers.fill_constant_batch_size_like(
label, shape=[-1, 1], value=1.0, dtype='float32')
right_cnt = fluid.layers.reduce_sum(input=fluid.layers.cast(
......
models/recall/word2vec/preprocess.py
浏览文件 @ 9b3afd7c
......@@ -162,7 +162,7 @@ def filter_corpus(args):
if r_value > keep_prob:
continue
write_line += str(idx)
write_line += ","
write_line += " "
signal = True
if signal:
write_line = write_line[:-1] + "\n"
......
models/recall/word2vec/w2v_evaluate_reader.py
浏览文件 @ 9b3afd7c
......@@ -20,10 +20,10 @@ from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class EvaluateReader(Reader):
class TrainReader(Reader):
def init(self):
dict_path = envs.get_global_env("word_id_dict_path", None,
"evaluate.reader")
dict_path = envs.get_global_env(
"dataset.dataset_infer.word_id_dict_path")
self.word_to_id = dict()
self.id_to_word = dict()
with io.open(dict_path, 'r', encoding='utf-8') as f:
......@@ -75,6 +75,8 @@ class EvaluateReader(Reader):
def generate_sample(self, line):
def reader():
if ':' in line:
pass
features = self.strip_lines(line.lower(), self.word_to_id)
features = features.split()
yield [('analogy_a', [self.word_to_id[features[0]]]),
......
models/recall/word2vec/w2v_reader.py
浏览文件 @ 9b3afd7c
......@@ -40,14 +40,12 @@ class NumpyRandomInt(object):
class TrainReader(Reader):
def init(self):
dict_path = envs.get_global_env("word_count_dict_path", None,
"train.reader")
self.window_size = envs.get_global_env("hyper_parameters.window_size",
None, "train.model")
self.neg_num = envs.get_global_env("hyper_parameters.neg_num", None,
"train.model")
dict_path = envs.get_global_env(
"dataset.dataset_train.word_count_dict_path")
self.window_size = envs.get_global_env("hyper_parameters.window_size")
self.neg_num = envs.get_global_env("hyper_parameters.neg_num")
self.with_shuffle_batch = envs.get_global_env(
"hyper_parameters.with_shuffle_batch", None, "train.model")
"hyper_parameters.with_shuffle_batch")
self.random_generator = NumpyRandomInt(1, self.window_size + 1)
self.cs = None
......
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