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未验证 提交 2ff6b226 编写于 作者: W wuzhihua 提交者： GitHub
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models/multitask/esmm/config.yaml
浏览文件 @ 2ff6b226
...@@ -12,40 +12,55 @@ ...@@ -12,40 +12,55 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
evaluate:
reader:
batch_size: 1
class: "{workspace}/esmm_infer_reader.py"
test_data_path: "{workspace}/data/train"
train: workspace: "paddlerec.models.multitask.esmm"
trainer:
# for cluster training
strategy: "async"
epochs: 3 dataset:
workspace: "paddlerec.models.multitask.esmm" - name: dataset_train
device: cpu batch_size: 1
type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/esmm_reader.py"
- name: dataset_infer
batch_size: 1
type: QueueDataset
data_path: "{workspace}/data/test"
data_converter: "{workspace}/esmm_reader.py"
reader: hyper_parameters:
batch_size: 2 vocab_size: 10000
class: "{workspace}/esmm_reader.py" embed_size: 128
train_data_path: "{workspace}/data/train" optimizer:
class: adam
learning_rate: 0.001
strategy: async
model: #use infer_runner mode and modify 'phase' below if infer
models: "{workspace}/model.py" mode: train_runner
hyper_parameters: #mode: infer_runner
vocab_size: 10000
embed_size: 128 runner:
learning_rate: 0.001 - name: train_runner
optimizer: adam class: single_train
device: cpu
epochs: 3
save_checkpoint_interval: 2
save_inference_interval: 4
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 10
- name: infer_runner
class: single_infer
init_model_path: "increment/0"
device: cpu
epochs: 3
save: phase:
increment: - name: train
dirname: "increment" model: "{workspace}/model.py"
epoch_interval: 2 dataset_name: dataset_train
save_last: True thread_num: 1
inference: #- name: infer
dirname: "inference" # model: "{workspace}/model.py"
epoch_interval: 4 # dataset_name: dataset_infer
save_last: True # thread_num: 1
models/multitask/esmm/esmm_infer_reader.py 已删除 100644 → 0
浏览文件 @ 8228c894
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
from __future__ import print_function
from collections import defaultdict
from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
def init(self):
all_field_id = [
'101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124',
'125', '126', '127', '128', '129', '205', '206', '207', '210',
'216', '508', '509', '702', '853', '301'
]
self.all_field_id_dict = defaultdict(int)
for i, field_id in enumerate(all_field_id):
self.all_field_id_dict[field_id] = [False, i]
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = line.strip().split(',')
ctr = int(features[1])
cvr = int(features[2])
padding = 0
output = [(field_id, []) for field_id in self.all_field_id_dict]
for elem in features[4:]:
field_id, feat_id = elem.strip().split(':')
if field_id not in self.all_field_id_dict:
continue
self.all_field_id_dict[field_id][0] = True
index = self.all_field_id_dict[field_id][1]
output[index][1].append(int(feat_id))
for field_id in self.all_field_id_dict:
visited, index = self.all_field_id_dict[field_id]
if visited:
self.all_field_id_dict[field_id][0] = False
else:
output[index][1].append(padding)
output.append(('ctr', [ctr]))
output.append(('cvr', [cvr]))
yield output
return reader
models/multitask/esmm/esmm_reader.py
浏览文件 @ 2ff6b226
...@@ -40,8 +40,6 @@ class TrainReader(Reader): ...@@ -40,8 +40,6 @@ class TrainReader(Reader):
This function needs to be implemented by the user, based on data format This function needs to be implemented by the user, based on data format
""" """
features = line.strip().split(',') features = line.strip().split(',')
# ctr = list(map(int, features[1]))
# cvr = list(map(int, features[2]))
ctr = int(features[1]) ctr = int(features[1])
cvr = int(features[2]) cvr = int(features[2])
...@@ -54,7 +52,6 @@ class TrainReader(Reader): ...@@ -54,7 +52,6 @@ class TrainReader(Reader):
continue continue
self.all_field_id_dict[field_id][0] = True self.all_field_id_dict[field_id][0] = True
index = self.all_field_id_dict[field_id][1] index = self.all_field_id_dict[field_id][1]
# feat_id = list(map(int, feat_id))
output[index][1].append(int(feat_id)) output[index][1].append(int(feat_id))
for field_id in self.all_field_id_dict: for field_id in self.all_field_id_dict:
......
models/multitask/esmm/model.py
浏览文件 @ 2ff6b226
...@@ -23,28 +23,11 @@ class Model(ModelBase): ...@@ -23,28 +23,11 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def fc(self, tag, data, out_dim, active='prelu'): def _init_hyper_parameters(self):
self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
self.embed_size = envs.get_global_env("hyper_parameters.embed_size")
init_stddev = 1.0 def input_data(self, is_infer=False, **kwargs):
scales = 1.0 / np.sqrt(data.shape[1])
p_attr = fluid.param_attr.ParamAttr(
name='%s_weight' % tag,
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=init_stddev * scales))
b_attr = fluid.ParamAttr(
name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
out = fluid.layers.fc(input=data,
size=out_dim,
act=active,
param_attr=p_attr,
bias_attr=b_attr,
name=tag)
return out
def input_data(self):
sparse_input_ids = [ sparse_input_ids = [
fluid.data( fluid.data(
name="field_" + str(i), name="field_" + str(i),
...@@ -55,26 +38,24 @@ class Model(ModelBase): ...@@ -55,26 +38,24 @@ class Model(ModelBase):
label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64") label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64")
label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64") label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64")
inputs = sparse_input_ids + [label_ctr] + [label_cvr] inputs = sparse_input_ids + [label_ctr] + [label_cvr]
self._data_var.extend(inputs) if is_infer:
return inputs
return inputs else:
return inputs
def net(self, inputs, is_infer=False): def net(self, inputs, is_infer=False):
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
self._namespace)
embed_size = envs.get_global_env("hyper_parameters.embed_size", None,
self._namespace)
emb = [] emb = []
# input feature data
for data in inputs[0:-2]: for data in inputs[0:-2]:
feat_emb = fluid.embedding( feat_emb = fluid.embedding(
input=data, input=data,
size=[vocab_size, embed_size], size=[self.vocab_size, self.embed_size],
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
name='dis_emb', name='dis_emb',
learning_rate=5, learning_rate=5,
initializer=fluid.initializer.Xavier( initializer=fluid.initializer.Xavier(
fan_in=embed_size, fan_out=embed_size)), fan_in=self.embed_size, fan_out=self.embed_size)),
is_sparse=True) is_sparse=True)
field_emb = fluid.layers.sequence_pool( field_emb = fluid.layers.sequence_pool(
input=feat_emb, pool_type='sum') input=feat_emb, pool_type='sum')
...@@ -83,14 +64,14 @@ class Model(ModelBase): ...@@ -83,14 +64,14 @@ class Model(ModelBase):
# ctr # ctr
active = 'relu' active = 'relu'
ctr_fc1 = self.fc('ctr_fc1', concat_emb, 200, active) ctr_fc1 = self._fc('ctr_fc1', concat_emb, 200, active)
ctr_fc2 = self.fc('ctr_fc2', ctr_fc1, 80, active) ctr_fc2 = self._fc('ctr_fc2', ctr_fc1, 80, active)
ctr_out = self.fc('ctr_out', ctr_fc2, 2, 'softmax') ctr_out = self._fc('ctr_out', ctr_fc2, 2, 'softmax')
# cvr # cvr
cvr_fc1 = self.fc('cvr_fc1', concat_emb, 200, active) cvr_fc1 = self._fc('cvr_fc1', concat_emb, 200, active)
cvr_fc2 = self.fc('cvr_fc2', cvr_fc1, 80, active) cvr_fc2 = self._fc('cvr_fc2', cvr_fc1, 80, active)
cvr_out = self.fc('cvr_out', cvr_fc2, 2, 'softmax') cvr_out = self._fc('cvr_out', cvr_fc2, 2, 'softmax')
ctr_clk = inputs[-2] ctr_clk = inputs[-2]
ctcvr_buy = inputs[-1] ctcvr_buy = inputs[-1]
...@@ -127,15 +108,23 @@ class Model(ModelBase): ...@@ -127,15 +108,23 @@ class Model(ModelBase):
self._metrics["AUC_ctcvr"] = auc_ctcvr self._metrics["AUC_ctcvr"] = auc_ctcvr
self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr
def train_net(self): def _fc(self, tag, data, out_dim, active='prelu'):
input_data = self.input_data()
self.net(input_data) init_stddev = 1.0
scales = 1.0 / np.sqrt(data.shape[1])
def infer_net(self):
self._infer_data_var = self.input_data() p_attr = fluid.param_attr.ParamAttr(
self._infer_data_loader = fluid.io.DataLoader.from_generator( name='%s_weight' % tag,
feed_list=self._infer_data_var, initializer=fluid.initializer.NormalInitializer(
capacity=64, loc=0.0, scale=init_stddev * scales))
use_double_buffer=False,
iterable=False) b_attr = fluid.ParamAttr(
self.net(self._infer_data_var, is_infer=True) name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
out = fluid.layers.fc(input=data,
size=out_dim,
act=active,
param_attr=p_attr,
bias_attr=b_attr,
name=tag)
return out
models/multitask/mmoe/census_infer_reader.py 已删除 100644 → 0
浏览文件 @ 8228c894
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
l = line.strip().split(',')
l = list(map(float, l))
label_income = []
label_marital = []
data = l[2:]
if int(l[1]) == 0:
label_income = [1, 0]
elif int(l[1]) == 1:
label_income = [0, 1]
if int(l[0]) == 0:
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
return reader
models/multitask/mmoe/config.yaml
浏览文件 @ 2ff6b226
...@@ -12,43 +12,57 @@ ...@@ -12,43 +12,57 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
evaluate: workspace: "paddlerec.models.multitask.mmoe"
reader:
batch_size: 1
class: "{workspace}/census_infer_reader.py"
test_data_path: "{workspace}/data/train"
train: dataset:
trainer: - name: dataset_train
# for cluster training batch_size: 1
strategy: "async" type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/census_reader.py"
- name: dataset_infer
batch_size: 1
type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/census_reader.py"
epochs: 3 hyper_parameters:
workspace: "paddlerec.models.multitask.mmoe" feature_size: 499
device: cpu expert_num: 8
gate_num: 2
expert_size: 16
tower_size: 8
optimizer:
class: adam
learning_rate: 0.001
strategy: async
reader: #use infer_runner mode and modify 'phase' below if infer
batch_size: 1 mode: train_runner
class: "{workspace}/census_reader.py" #mode: infer_runner
train_data_path: "{workspace}/data/train"
model: runner:
models: "{workspace}/model.py" - name: train_runner
hyper_parameters: class: single_train
feature_size: 499 device: cpu
expert_num: 8 epochs: 3
gate_num: 2 save_checkpoint_interval: 2
expert_size: 16 save_inference_interval: 4
tower_size: 8 save_checkpoint_path: "increment"
learning_rate: 0.001 save_inference_path: "inference"
optimizer: adam print_interval: 10
- name: infer_runner
class: single_infer
init_model_path: "increment/0"
device: cpu
epochs: 3
save: phase:
increment: - name: train
dirname: "increment" model: "{workspace}/model.py"
epoch_interval: 2 dataset_name: dataset_train
save_last: True thread_num: 1
inference: #- name: infer
dirname: "inference" # model: "{workspace}/model.py"
epoch_interval: 4 # dataset_name: dataset_infer
save_last: True # thread_num: 1
models/multitask/mmoe/data/run.sh 0 → 100644
浏览文件 @ 2ff6b226
mkdir train_data
mkdir test_data
mkdir data
train_path="data/census-income.data"
test_path="data/census-income.test"
train_data_path="train_data/"
test_data_path="test_data/"
pip install -r requirements.txt
wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census.tar.gz
tar -zxvf data/census.tar.gz -C data/
python data_preparation.py --train_path ${train_path} \
--test_path ${test_path} \
--train_data_path ${train_data_path}\
--test_data_path ${test_data_path}
models/multitask/mmoe/model.py
浏览文件 @ 2ff6b226
...@@ -22,53 +22,51 @@ class Model(ModelBase): ...@@ -22,53 +22,51 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def MMOE(self, is_infer=False): def _init_hyper_parameters(self):
feature_size = envs.get_global_env("hyper_parameters.feature_size", self.feature_size = envs.get_global_env(
None, self._namespace) "hyper_parameters.feature_size")
expert_num = envs.get_global_env("hyper_parameters.expert_num", None, self.expert_num = envs.get_global_env("hyper_parameters.expert_num")
self._namespace) self.gate_num = envs.get_global_env("hyper_parameters.gate_num")
gate_num = envs.get_global_env("hyper_parameters.gate_num", None, self.expert_size = envs.get_global_env("hyper_parameters.expert_size")
self._namespace) self.tower_size = envs.get_global_env("hyper_parameters.tower_size")
expert_size = envs.get_global_env("hyper_parameters.expert_size", None,
self._namespace) def input_data(self, is_infer=False, **kwargs):
tower_size = envs.get_global_env("hyper_parameters.tower_size", None, inputs = fluid.data(
self._namespace) name="input", shape=[-1, self.feature_size], dtype="float32")
input_data = fluid.data(
name="input", shape=[-1, feature_size], dtype="float32")
label_income = fluid.data( label_income = fluid.data(
name="label_income", shape=[-1, 2], dtype="float32", lod_level=0) name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
label_marital = fluid.data( label_marital = fluid.data(
name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0) name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
if is_infer: if is_infer:
self._infer_data_var = [input_data, label_income, label_marital] return [inputs, label_income, label_marital]
self._infer_data_loader = fluid.io.DataLoader.from_generator( else:
feed_list=self._infer_data_var, return [inputs, label_income, label_marital]
capacity=64,
use_double_buffer=False, def net(self, inputs, is_infer=False):
iterable=False) input_data = inputs[0]
label_income = inputs[1]
self._data_var.extend([input_data, label_income, label_marital]) label_marital = inputs[2]
# f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper # f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper
expert_outputs = [] expert_outputs = []
for i in range(0, expert_num): for i in range(0, self.expert_num):
expert_output = fluid.layers.fc( expert_output = fluid.layers.fc(
input=input_data, input=input_data,
size=expert_size, size=self.expert_size,
act='relu', act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0), bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='expert_' + str(i)) name='expert_' + str(i))
expert_outputs.append(expert_output) expert_outputs.append(expert_output)
expert_concat = fluid.layers.concat(expert_outputs, axis=1) expert_concat = fluid.layers.concat(expert_outputs, axis=1)
expert_concat = fluid.layers.reshape(expert_concat, expert_concat = fluid.layers.reshape(
[-1, expert_num, expert_size]) expert_concat, [-1, self.expert_num, self.expert_size])
# g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper # g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
output_layers = [] output_layers = []
for i in range(0, gate_num): for i in range(0, self.gate_num):
cur_gate = fluid.layers.fc( cur_gate = fluid.layers.fc(
input=input_data, input=input_data,
size=expert_num, size=self.expert_num,
act='softmax', act='softmax',
bias_attr=fluid.ParamAttr(learning_rate=1.0), bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='gate_' + str(i)) name='gate_' + str(i))
...@@ -78,7 +76,7 @@ class Model(ModelBase): ...@@ -78,7 +76,7 @@ class Model(ModelBase):
cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1) cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1)
# Build tower layer # Build tower layer
cur_tower = fluid.layers.fc(input=cur_gate_expert, cur_tower = fluid.layers.fc(input=cur_gate_expert,
size=tower_size, size=self.tower_size,
act='relu', act='relu',
name='task_layer_' + str(i)) name='task_layer_' + str(i))
out = fluid.layers.fc(input=cur_tower, out = fluid.layers.fc(input=cur_tower,
...@@ -127,8 +125,5 @@ class Model(ModelBase): ...@@ -127,8 +125,5 @@ class Model(ModelBase):
self._metrics["AUC_marital"] = auc_marital self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2 self._metrics["BATCH_AUC_marital"] = batch_auc_2
def train_net(self):
self.MMOE()
def infer_net(self): def infer_net(self):
self.MMOE(is_infer=True) pass
models/multitask/readme.md
浏览文件 @ 2ff6b226
...@@ -9,7 +9,9 @@ ...@@ -9,7 +9,9 @@
* [整体介绍](#整体介绍) * [整体介绍](#整体介绍)
* [多任务模型列表](#多任务模型列表) * [多任务模型列表](#多任务模型列表)
* [使用教程](#使用教程) * [使用教程](#使用教程)
* [训练&预测](#训练&预测) * [数据处理](#数据处理)
* [训练](#训练)
* [预测](#预测)
* [效果对比](#效果对比) * [效果对比](#效果对比)
* [模型效果列表](#模型效果列表) * [模型效果列表](#模型效果列表)
...@@ -40,14 +42,49 @@ ...@@ -40,14 +42,49 @@
<img align="center" src="../../doc/imgs/mmoe.png"> <img align="center" src="../../doc/imgs/mmoe.png">
<p> <p>
## 使用教程 ## 使用教程(快速开始)
### 训练&预测
```shell ```shell
python -m paddlerec.run -m paddlerec.models.multitask.mmoe # mmoe python -m paddlerec.run -m paddlerec.models.multitask.mmoe # mmoe
python -m paddlerec.run -m paddlerec.models.multitask.share-bottom # share-bottom python -m paddlerec.run -m paddlerec.models.multitask.share-bottom # share-bottom
python -m paddlerec.run -m paddlerec.models.multitask.esmm # esmm python -m paddlerec.run -m paddlerec.models.multitask.esmm # esmm
``` ```
## 使用教程（复现论文）
### 注意
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 |
| :------------------: | :--------------------: | :--------------------: | :--------------------: |
| Share-Bottom | 32 | 1 | 400 |
| MMoE | 32 | 1 | 400 |
| ESMM | 64 | 2 | 100 |
### 数据处理
参考每个模型目录数据下载&预处理脚本
```
sh run.sh
```
### 训练
```
cd modles/multitask/mmoe # 进入选定好的排序模型的目录 以MMoE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MMoE为例
```
## 效果对比 ## 效果对比
### 模型效果列表 ### 模型效果列表
......
models/multitask/share-bottom/census_infer_reader.py 已删除 100644 → 0
浏览文件 @ 8228c894
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
l = line.strip().split(',')
l = list(map(float, l))
label_income = []
label_marital = []
data = l[2:]
if int(l[1]) == 0:
label_income = [1, 0]
elif int(l[1]) == 1:
label_income = [0, 1]
if int(l[0]) == 0:
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
return reader
models/multitask/share-bottom/config.yaml
浏览文件 @ 2ff6b226
...@@ -12,42 +12,56 @@ ...@@ -12,42 +12,56 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
evaluate: workspace: "paddlerec.models.multitask.share-bottom"
reader:
batch_size: 1
class: "{workspace}/census_infer_reader.py"
test_data_path: "{workspace}/data/train"
train: dataset:
trainer: - name: dataset_train
# for cluster training batch_size: 1
strategy: "async" type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/census_reader.py"
- name: dataset_infer
batch_size: 1
type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/census_reader.py"
epochs: 3 hyper_parameters:
workspace: "paddlerec.models.multitask.share-bottom" feature_size: 499
device: cpu bottom_size: 117
tower_nums: 2
tower_size: 8
optimizer:
class: adam
learning_rate: 0.001
strategy: async
reader: #use infer_runner mode and modify 'phase' below if infer
batch_size: 2 mode: train_runner
class: "{workspace}/census_reader.py" #mode: infer_runner
train_data_path: "{workspace}/data/train"
model: runner:
models: "{workspace}/model.py" - name: train_runner
hyper_parameters: class: single_train
feature_size: 499 device: cpu
bottom_size: 117 epochs: 3
tower_nums: 2 save_checkpoint_interval: 2
tower_size: 8 save_inference_interval: 4
learning_rate: 0.001 save_checkpoint_path: "increment"
optimizer: adam save_inference_path: "inference"
print_interval: 5
- name: infer_runner
class: single_infer
init_model_path: "increment/0"
device: cpu
epochs: 3
save: phase:
increment: - name: train
dirname: "increment" model: "{workspace}/model.py"
epoch_interval: 2 dataset_name: dataset_train
save_last: True thread_num: 1
inference: #- name: infer
dirname: "inference" # model: "{workspace}/model.py"
epoch_interval: 4 # dataset_name: dataset_infer
save_last: True # thread_num: 1
models/multitask/share-bottom/model.py
浏览文件 @ 2ff6b226
...@@ -22,46 +22,42 @@ class Model(ModelBase): ...@@ -22,46 +22,42 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def model(self, is_infer=False): def _init_hyper_parameters(self):
self.feature_size = envs.get_global_env(
feature_size = envs.get_global_env("hyper_parameters.feature_size", "hyper_parameters.feature_size")
None, self._namespace) self.bottom_size = envs.get_global_env("hyper_parameters.bottom_size")
bottom_size = envs.get_global_env("hyper_parameters.bottom_size", None, self.tower_size = envs.get_global_env("hyper_parameters.tower_size")
self._namespace) self.tower_nums = envs.get_global_env("hyper_parameters.tower_nums")
tower_size = envs.get_global_env("hyper_parameters.tower_size", None,
self._namespace) def input_data(self, is_infer=False, **kwargs):
tower_nums = envs.get_global_env("hyper_parameters.tower_nums", None, inputs = fluid.data(
self._namespace) name="input", shape=[-1, self.feature_size], dtype="float32")
input_data = fluid.data(
name="input", shape=[-1, feature_size], dtype="float32")
label_income = fluid.data( label_income = fluid.data(
name="label_income", shape=[-1, 2], dtype="float32", lod_level=0) name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
label_marital = fluid.data( label_marital = fluid.data(
name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0) name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
if is_infer: if is_infer:
self._infer_data_var = [input_data, label_income, label_marital] return [inputs, label_income, label_marital]
self._infer_data_loader = fluid.io.DataLoader.from_generator( else:
feed_list=self._infer_data_var, return [inputs, label_income, label_marital]
capacity=64,
use_double_buffer=False,
iterable=False)
self._data_var.extend([input_data, label_income, label_marital]) def net(self, inputs, is_infer=False):
input_data = inputs[0]
label_income = inputs[1]
label_marital = inputs[2]
bottom_output = fluid.layers.fc( bottom_output = fluid.layers.fc(
input=input_data, input=input_data,
size=bottom_size, size=self.bottom_size,
act='relu', act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0), bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='bottom_output') name='bottom_output')
# Build tower layer from bottom layer # Build tower layer from bottom layer
output_layers = [] output_layers = []
for index in range(tower_nums): for index in range(self.tower_nums):
tower_layer = fluid.layers.fc(input=bottom_output, tower_layer = fluid.layers.fc(input=bottom_output,
size=tower_size, size=self.tower_size,
act='relu', act='relu',
name='task_layer_' + str(index)) name='task_layer_' + str(index))
output_layer = fluid.layers.fc(input=tower_layer, output_layer = fluid.layers.fc(input=tower_layer,
...@@ -107,9 +103,3 @@ class Model(ModelBase): ...@@ -107,9 +103,3 @@ class Model(ModelBase):
self._metrics["BATCH_AUC_income"] = batch_auc_1 self._metrics["BATCH_AUC_income"] = batch_auc_1
self._metrics["AUC_marital"] = auc_marital self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2 self._metrics["BATCH_AUC_marital"] = batch_auc_2
def train_net(self):
self.model()
def infer_net(self):
self.model(is_infer=True)
models/recall/gru4rec/config.yaml
浏览文件 @ 2ff6b226
...@@ -12,47 +12,59 @@ ...@@ -12,47 +12,59 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
evaluate: workspace: "paddlerec.models.recall.gru4rec"
reader:
batch_size: 1
class: "{workspace}/rsc15_infer_reader.py"
test_data_path: "{workspace}/data/train"
is_return_numpy: False
dataset:
- name: dataset_train
batch_size: 5
type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/rsc15_reader.py"
- name: dataset_infer
batch_size: 5
type: QueueDataset
data_path: "{workspace}/data/test"
data_converter: "{workspace}/rsc15_reader.py"
train: hyper_parameters:
trainer: vocab_size: 1000
# for cluster training hid_size: 100
strategy: "async" emb_lr_x: 10.0
gru_lr_x: 1.0
fc_lr_x: 1.0
init_low_bound: -0.04
init_high_bound: 0.04
optimizer:
class: adagrad
learning_rate: 0.01
strategy: async
#use infer_runner mode and modify 'phase' below if infer
mode: train_runner
#mode: infer_runner
runner:
- name: train_runner
class: single_train
device: cpu
epochs: 3 epochs: 3
workspace: "paddlerec.models.recall.gru4rec" save_checkpoint_interval: 2
save_inference_interval: 4
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 10
- name: infer_runner
class: single_infer
init_model_path: "increment/0"
device: cpu device: cpu
epochs: 3
reader: phase:
batch_size: 5 - name: train
class: "{workspace}/rsc15_reader.py" model: "{workspace}/model.py"
train_data_path: "{workspace}/data/train" dataset_name: dataset_train
thread_num: 1
model: #- name: infer
models: "{workspace}/model.py" # model: "{workspace}/model.py"
hyper_parameters: # dataset_name: dataset_infer
vocab_size: 1000 # thread_num: 1
hid_size: 100
emb_lr_x: 10.0
gru_lr_x: 1.0
fc_lr_x: 1.0
init_low_bound: -0.04
init_high_bound: 0.04
learning_rate: 0.01
optimizer: adagrad
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/recall/gru4rec/model.py
浏览文件 @ 2ff6b226
...@@ -22,84 +22,72 @@ class Model(ModelBase): ...@@ -22,84 +22,72 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def all_vocab_network(self, is_infer=False): def _init_hyper_parameters(self):
""" network definition """ self.recall_k = envs.get_global_env("hyper_parameters.recall_k")
recall_k = envs.get_global_env("hyper_parameters.recall_k", None, self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
self._namespace) self.hid_size = envs.get_global_env("hyper_parameters.hid_size")
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self.init_low_bound = envs.get_global_env(
self._namespace) "hyper_parameters.init_low_bound")
hid_size = envs.get_global_env("hyper_parameters.hid_size", None, self.init_high_bound = envs.get_global_env(
self._namespace) "hyper_parameters.init_high_bound")
init_low_bound = envs.get_global_env("hyper_parameters.init_low_bound", self.emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x")
None, self._namespace) self.gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x")
init_high_bound = envs.get_global_env( self.fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x")
"hyper_parameters.init_high_bound", None, self._namespace)
emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x", None, def input_data(self, is_infer=False, **kwargs):
self._namespace)
gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x", None,
self._namespace)
fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x", None,
self._namespace)
# Input data # Input data
src_wordseq = fluid.data( src_wordseq = fluid.data(
name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1) name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
dst_wordseq = fluid.data( dst_wordseq = fluid.data(
name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1) name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
if is_infer: return [src_wordseq, dst_wordseq]
self._infer_data_var = [src_wordseq, dst_wordseq]
self._infer_data_loader = fluid.io.DataLoader.from_generator( def net(self, inputs, is_infer=False):
feed_list=self._infer_data_var, src_wordseq = inputs[0]
capacity=64, dst_wordseq = inputs[1]
use_double_buffer=False,
iterable=False)
emb = fluid.embedding( emb = fluid.embedding(
input=src_wordseq, input=src_wordseq,
size=[vocab_size, hid_size], size=[self.vocab_size, self.hid_size],
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
name="emb", name="emb",
initializer=fluid.initializer.Uniform( initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound), low=self.init_low_bound, high=self.init_high_bound),
learning_rate=emb_lr_x), learning_rate=self.emb_lr_x),
is_sparse=True) is_sparse=True)
fc0 = fluid.layers.fc(input=emb, fc0 = fluid.layers.fc(input=emb,
size=hid_size * 3, size=self.hid_size * 3,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform( initializer=fluid.initializer.Uniform(
low=init_low_bound, low=self.init_low_bound,
high=init_high_bound), high=self.init_high_bound),
learning_rate=gru_lr_x)) learning_rate=self.gru_lr_x))
gru_h0 = fluid.layers.dynamic_gru( gru_h0 = fluid.layers.dynamic_gru(
input=fc0, input=fc0,
size=hid_size, size=self.hid_size,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform( initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound), low=self.init_low_bound, high=self.init_high_bound),
learning_rate=gru_lr_x)) learning_rate=self.gru_lr_x))
fc = fluid.layers.fc(input=gru_h0, fc = fluid.layers.fc(input=gru_h0,
size=vocab_size, size=self.vocab_size,
act='softmax', act='softmax',
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform( initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound), low=self.init_low_bound,
learning_rate=fc_lr_x)) high=self.init_high_bound),
learning_rate=self.fc_lr_x))
cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq) cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq)
acc = fluid.layers.accuracy(input=fc, label=dst_wordseq, k=recall_k) acc = fluid.layers.accuracy(
input=fc, label=dst_wordseq, k=self.recall_k)
if is_infer: if is_infer:
self._infer_results['recall20'] = acc self._infer_results['recall20'] = acc
return return
avg_cost = fluid.layers.mean(x=cost) avg_cost = fluid.layers.mean(x=cost)
self._data_var.append(src_wordseq)
self._data_var.append(dst_wordseq)
self._cost = avg_cost self._cost = avg_cost
self._metrics["cost"] = avg_cost self._metrics["cost"] = avg_cost
self._metrics["acc"] = acc self._metrics["acc"] = acc
def train_net(self):
self.all_vocab_network()
def infer_net(self):
self.all_vocab_network(is_infer=True)
models/recall/gru4rec/rsc15_infer_reader.py 已删除 100644 → 0
浏览文件 @ 8228c894
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
l = line.strip().split()
l = [w for w in l]
src_seq = l[:len(l) - 1]
src_seq = [int(e) for e in src_seq]
trg_seq = l[1:]
trg_seq = [int(e) for e in trg_seq]
feature_name = ["src_wordseq", "dst_wordseq"]
yield zip(feature_name, [src_seq] + [trg_seq])
return reader
models/recall/ncf/config.yaml
浏览文件 @ 2ff6b226
...@@ -12,42 +12,56 @@ ...@@ -12,42 +12,56 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
evaluate: workspace: "paddlerec.models.recall.ncf"
reader:
batch_size: 1
class: "{workspace}/movielens_infer_reader.py"
test_data_path: "{workspace}/data/test"
train: dataset:
trainer: - name: dataset_train
# for cluster training batch_size: 5
strategy: "async" type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/movielens_reader.py"
- name: dataset_infer
batch_size: 5
type: QueueDataset
data_path: "{workspace}/data/test"
data_converter: "{workspace}/movielens_infer_reader.py"
epochs: 3 hyper_parameters:
workspace: "paddlerec.models.recall.ncf" num_users: 6040
device: cpu num_items: 3706
latent_dim: 8
fc_layers: [64, 32, 16, 8]
optimizer:
class: adam
learning_rate: 0.001
strategy: async
reader: #use infer_runner mode and modify 'phase' below if infer
batch_size: 2 mode: train_runner
class: "{workspace}/movielens_reader.py" #mode: infer_runner
train_data_path: "{workspace}/data/train"
model: runner:
models: "{workspace}/model.py" - name: train_runner
hyper_parameters: class: single_train
num_users: 6040 device: cpu
num_items: 3706 epochs: 3
latent_dim: 8 save_checkpoint_interval: 2
layers: [64, 32, 16, 8] save_inference_interval: 4
learning_rate: 0.001 save_checkpoint_path: "increment"
optimizer: adam save_inference_path: "inference"
print_interval: 10
- name: infer_runner
class: single_infer
init_model_path: "increment/0"
device: cpu
epochs: 3
save: phase:
increment: - name: train
dirname: "increment" model: "{workspace}/model.py"
epoch_interval: 2 dataset_name: dataset_train
save_last: True thread_num: 1
inference: #- name: infer
dirname: "inference" # model: "{workspace}/model.py"
epoch_interval: 4 # dataset_name: dataset_infer
save_last: True # thread_num: 1
models/recall/ncf/model.py
浏览文件 @ 2ff6b226
...@@ -24,7 +24,13 @@ class Model(ModelBase): ...@@ -24,7 +24,13 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def input_data(self, is_infer=False): def _init_hyper_parameters(self):
self.num_users = envs.get_global_env("hyper_parameters.num_users")
self.num_items = envs.get_global_env("hyper_parameters.num_items")
self.latent_dim = envs.get_global_env("hyper_parameters.latent_dim")
self.layers = envs.get_global_env("hyper_parameters.fc_layers")
def input_data(self, is_infer=False, **kwargs):
user_input = fluid.data( user_input = fluid.data(
name="user_input", shape=[-1, 1], dtype="int64", lod_level=0) name="user_input", shape=[-1, 1], dtype="int64", lod_level=0)
item_input = fluid.data( item_input = fluid.data(
...@@ -35,45 +41,35 @@ class Model(ModelBase): ...@@ -35,45 +41,35 @@ class Model(ModelBase):
inputs = [user_input] + [item_input] inputs = [user_input] + [item_input]
else: else:
inputs = [user_input] + [item_input] + [label] inputs = [user_input] + [item_input] + [label]
self._data_var = inputs
return inputs return inputs
def net(self, inputs, is_infer=False): def net(self, inputs, is_infer=False):
num_users = envs.get_global_env("hyper_parameters.num_users", None, num_layer = len(self.layers) #Number of layers in the MLP
self._namespace)
num_items = envs.get_global_env("hyper_parameters.num_items", None,
self._namespace)
latent_dim = envs.get_global_env("hyper_parameters.latent_dim", None,
self._namespace)
layers = envs.get_global_env("hyper_parameters.layers", None,
self._namespace)
num_layer = len(layers) #Number of layers in the MLP
MF_Embedding_User = fluid.embedding( MF_Embedding_User = fluid.embedding(
input=inputs[0], input=inputs[0],
size=[num_users, latent_dim], size=[self.num_users, self.latent_dim],
param_attr=fluid.initializer.Normal( param_attr=fluid.initializer.Normal(
loc=0.0, scale=0.01), loc=0.0, scale=0.01),
is_sparse=True) is_sparse=True)
MF_Embedding_Item = fluid.embedding( MF_Embedding_Item = fluid.embedding(
input=inputs[1], input=inputs[1],
size=[num_items, latent_dim], size=[self.num_items, self.latent_dim],
param_attr=fluid.initializer.Normal( param_attr=fluid.initializer.Normal(
loc=0.0, scale=0.01), loc=0.0, scale=0.01),
is_sparse=True) is_sparse=True)
MLP_Embedding_User = fluid.embedding( MLP_Embedding_User = fluid.embedding(
input=inputs[0], input=inputs[0],
size=[num_users, int(layers[0] / 2)], size=[self.num_users, int(self.layers[0] / 2)],
param_attr=fluid.initializer.Normal( param_attr=fluid.initializer.Normal(
loc=0.0, scale=0.01), loc=0.0, scale=0.01),
is_sparse=True) is_sparse=True)
MLP_Embedding_Item = fluid.embedding( MLP_Embedding_Item = fluid.embedding(
input=inputs[1], input=inputs[1],
size=[num_items, int(layers[0] / 2)], size=[self.num_items, int(self.layers[0] / 2)],
param_attr=fluid.initializer.Normal( param_attr=fluid.initializer.Normal(
loc=0.0, scale=0.01), loc=0.0, scale=0.01),
is_sparse=True) is_sparse=True)
...@@ -94,7 +90,7 @@ class Model(ModelBase): ...@@ -94,7 +90,7 @@ class Model(ModelBase):
for i in range(1, num_layer): for i in range(1, num_layer):
mlp_vector = fluid.layers.fc( mlp_vector = fluid.layers.fc(
input=mlp_vector, input=mlp_vector,
size=layers[i], size=self.layers[i],
act='relu', act='relu',
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormal( initializer=fluid.initializer.TruncatedNormal(
...@@ -126,16 +122,3 @@ class Model(ModelBase): ...@@ -126,16 +122,3 @@ class Model(ModelBase):
self._cost = avg_cost self._cost = avg_cost
self._metrics["cost"] = avg_cost self._metrics["cost"] = avg_cost
def train_net(self):
input_data = self.input_data()
self.net(input_data)
def infer_net(self):
self._infer_data_var = self.input_data(is_infer=True)
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
self.net(self._infer_data_var, is_infer=True)
models/recall/ncf/movielens_infer_reader.py
浏览文件 @ 2ff6b226
...@@ -19,7 +19,7 @@ from collections import defaultdict ...@@ -19,7 +19,7 @@ from collections import defaultdict
import numpy as np import numpy as np
class EvaluateReader(Reader): class TrainReader(Reader):
def init(self): def init(self):
pass pass
......
models/recall/ssr/config.yaml
浏览文件 @ 2ff6b226
...@@ -12,43 +12,55 @@ ...@@ -12,43 +12,55 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
workspace: "paddlerec.models.recall.ssr"
evaluate: dataset:
reader: - name: dataset_train
batch_size: 1 batch_size: 5
class: "{workspace}/ssr_infer_reader.py" type: QueueDataset
test_data_path: "{workspace}/data/train" data_path: "{workspace}/data/train"
is_return_numpy: True data_converter: "{workspace}/ssr_reader.py"
- name: dataset_infer
batch_size: 5
type: QueueDataset
data_path: "{workspace}/data/test"
data_converter: "{workspace}/ssr_infer_reader.py"
train: hyper_parameters:
trainer: vocab_size: 1000
# for cluster training emb_dim: 128
strategy: "async" hidden_size: 100
optimizer:
class: adagrad
learning_rate: 0.01
strategy: async
#use infer_runner mode and modify 'phase' below if infer
mode: train_runner
#mode: infer_runner
runner:
- name: train_runner
class: single_train
device: cpu
epochs: 3 epochs: 3
workspace: "paddlerec.models.recall.ssr" save_checkpoint_interval: 2
save_inference_interval: 4
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 10
- name: infer_runner
class: single_infer
init_model_path: "increment/0"
device: cpu device: cpu
epochs: 3
reader: phase:
batch_size: 5 - name: train
class: "{workspace}/ssr_reader.py" model: "{workspace}/model.py"
train_data_path: "{workspace}/data/train" dataset_name: dataset_train
thread_num: 1
model: #- name: infer
models: "{workspace}/model.py" # model: "{workspace}/model.py"
hyper_parameters: # dataset_name: dataset_infer
vocab_size: 1000 # thread_num: 1
emb_dim: 128
hidden_size: 100
learning_rate: 0.01
optimizer: adagrad
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/recall/ssr/model.py
浏览文件 @ 2ff6b226
...@@ -20,85 +20,45 @@ from paddlerec.core.utils import envs ...@@ -20,85 +20,45 @@ from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase from paddlerec.core.model import Model as ModelBase
class BowEncoder(object):
""" bow-encoder """
def __init__(self):
self.param_name = ""
def forward(self, emb):
return fluid.layers.sequence_pool(input=emb, pool_type='sum')
class GrnnEncoder(object):
""" grnn-encoder """
def __init__(self, param_name="grnn", hidden_size=128):
self.param_name = param_name
self.hidden_size = hidden_size
def forward(self, emb):
fc0 = fluid.layers.fc(input=emb,
size=self.hidden_size * 3,
param_attr=self.param_name + "_fc.w",
bias_attr=False)
gru_h = fluid.layers.dynamic_gru(
input=fc0,
size=self.hidden_size,
is_reverse=False,
param_attr=self.param_name + ".param",
bias_attr=self.param_name + ".bias")
return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
class PairwiseHingeLoss(object):
def __init__(self, margin=0.8):
self.margin = margin
def forward(self, pos, neg):
loss_part1 = fluid.layers.elementwise_sub(
tensor.fill_constant_batch_size_like(
input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
pos)
loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
loss_part3 = fluid.layers.elementwise_max(
tensor.fill_constant_batch_size_like(
input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_part2)
return loss_part3
class Model(ModelBase): class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def get_correct(self, x, y): def _init_hyper_parameters(self):
less = tensor.cast(cf.less_than(x, y), dtype='float32') self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
correct = fluid.layers.reduce_sum(less) self.emb_dim = envs.get_global_env("hyper_parameters.emb_dim")
return correct self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
def train(self): def input_data(self, is_infer=False, **kwargs):
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, if is_infer:
self._namespace) user_data = fluid.data(
emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None, name="user", shape=[None, 1], dtype="int64", lod_level=1)
self._namespace) all_item_data = fluid.data(
hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, name="all_item", shape=[None, self.vocab_size], dtype="int64")
self._namespace) pos_label = fluid.data(
emb_shape = [vocab_size, emb_dim] name="pos_label", shape=[None, 1], dtype="int64")
return [user_data, all_item_data, pos_label]
else:
user_data = fluid.data(
name="user", shape=[None, 1], dtype="int64", lod_level=1)
pos_item_data = fluid.data(
name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
neg_item_data = fluid.data(
name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
return [user_data, pos_item_data, neg_item_data]
def net(self, inputs, is_infer=False):
if is_infer:
self._infer_net(inputs)
return
user_data = inputs[0]
pos_item_data = inputs[1]
neg_item_data = inputs[2]
emb_shape = [self.vocab_size, self.emb_dim]
self.user_encoder = GrnnEncoder() self.user_encoder = GrnnEncoder()
self.item_encoder = BowEncoder() self.item_encoder = BowEncoder()
self.pairwise_hinge_loss = PairwiseHingeLoss() self.pairwise_hinge_loss = PairwiseHingeLoss()
user_data = fluid.data(
name="user", shape=[None, 1], dtype="int64", lod_level=1)
pos_item_data = fluid.data(
name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
neg_item_data = fluid.data(
name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
self._data_var.extend([user_data, pos_item_data, neg_item_data])
user_emb = fluid.embedding( user_emb = fluid.embedding(
input=user_data, size=emb_shape, param_attr="emb.item") input=user_data, size=emb_shape, param_attr="emb.item")
pos_item_emb = fluid.embedding( pos_item_emb = fluid.embedding(
...@@ -109,79 +69,115 @@ class Model(ModelBase): ...@@ -109,79 +69,115 @@ class Model(ModelBase):
pos_item_enc = self.item_encoder.forward(pos_item_emb) pos_item_enc = self.item_encoder.forward(pos_item_emb)
neg_item_enc = self.item_encoder.forward(neg_item_emb) neg_item_enc = self.item_encoder.forward(neg_item_emb)
user_hid = fluid.layers.fc(input=user_enc, user_hid = fluid.layers.fc(input=user_enc,
size=hidden_size, size=self.hidden_size,
param_attr='user.w', param_attr='user.w',
bias_attr="user.b") bias_attr="user.b")
pos_item_hid = fluid.layers.fc(input=pos_item_enc, pos_item_hid = fluid.layers.fc(input=pos_item_enc,
size=hidden_size, size=self.hidden_size,
param_attr='item.w', param_attr='item.w',
bias_attr="item.b") bias_attr="item.b")
neg_item_hid = fluid.layers.fc(input=neg_item_enc, neg_item_hid = fluid.layers.fc(input=neg_item_enc,
size=hidden_size, size=self.hidden_size,
param_attr='item.w', param_attr='item.w',
bias_attr="item.b") bias_attr="item.b")
cos_pos = fluid.layers.cos_sim(user_hid, pos_item_hid) cos_pos = fluid.layers.cos_sim(user_hid, pos_item_hid)
cos_neg = fluid.layers.cos_sim(user_hid, neg_item_hid) cos_neg = fluid.layers.cos_sim(user_hid, neg_item_hid)
hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg) hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
avg_cost = fluid.layers.mean(hinge_loss) avg_cost = fluid.layers.mean(hinge_loss)
correct = self.get_correct(cos_neg, cos_pos) correct = self._get_correct(cos_neg, cos_pos)
self._cost = avg_cost self._cost = avg_cost
self._metrics["correct"] = correct self._metrics["correct"] = correct
self._metrics["hinge_loss"] = hinge_loss self._metrics["hinge_loss"] = hinge_loss
def train_net(self): def _infer_net(self, inputs):
self.train() user_data = inputs[0]
all_item_data = inputs[1]
def infer(self): pos_label = inputs[2]
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
self._namespace)
emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None,
self._namespace)
hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None,
self._namespace)
user_data = fluid.data(
name="user", shape=[None, 1], dtype="int64", lod_level=1)
all_item_data = fluid.data(
name="all_item", shape=[None, vocab_size], dtype="int64")
pos_label = fluid.data(
name="pos_label", shape=[None, 1], dtype="int64")
self._infer_data_var = [user_data, all_item_data, pos_label]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
user_emb = fluid.embedding( user_emb = fluid.embedding(
input=user_data, size=[vocab_size, emb_dim], param_attr="emb.item") input=user_data,
size=[self.vocab_size, self.emb_dim],
param_attr="emb.item")
all_item_emb = fluid.embedding( all_item_emb = fluid.embedding(
input=all_item_data, input=all_item_data,
size=[vocab_size, emb_dim], size=[self.vocab_size, self.emb_dim],
param_attr="emb.item") param_attr="emb.item")
all_item_emb_re = fluid.layers.reshape( all_item_emb_re = fluid.layers.reshape(
x=all_item_emb, shape=[-1, emb_dim]) x=all_item_emb, shape=[-1, self.emb_dim])
user_encoder = GrnnEncoder() user_encoder = GrnnEncoder()
user_enc = user_encoder.forward(user_emb) user_enc = user_encoder.forward(user_emb)
user_hid = fluid.layers.fc(input=user_enc, user_hid = fluid.layers.fc(input=user_enc,
size=hidden_size, size=self.hidden_size,
param_attr='user.w', param_attr='user.w',
bias_attr="user.b") bias_attr="user.b")
user_exp = fluid.layers.expand( user_exp = fluid.layers.expand(
x=user_hid, expand_times=[1, vocab_size]) x=user_hid, expand_times=[1, self.vocab_size])
user_re = fluid.layers.reshape(x=user_exp, shape=[-1, hidden_size]) user_re = fluid.layers.reshape(
x=user_exp, shape=[-1, self.hidden_size])
all_item_hid = fluid.layers.fc(input=all_item_emb_re, all_item_hid = fluid.layers.fc(input=all_item_emb_re,
size=hidden_size, size=self.hidden_size,
param_attr='item.w', param_attr='item.w',
bias_attr="item.b") bias_attr="item.b")
cos_item = fluid.layers.cos_sim(X=all_item_hid, Y=user_re) cos_item = fluid.layers.cos_sim(X=all_item_hid, Y=user_re)
all_pre_ = fluid.layers.reshape(x=cos_item, shape=[-1, vocab_size]) all_pre_ = fluid.layers.reshape(
x=cos_item, shape=[-1, self.vocab_size])
acc = fluid.layers.accuracy(input=all_pre_, label=pos_label, k=20) acc = fluid.layers.accuracy(input=all_pre_, label=pos_label, k=20)
self._infer_results['recall20'] = acc self._infer_results['recall20'] = acc
def infer_net(self): def _get_correct(self, x, y):
self.infer() less = tensor.cast(cf.less_than(x, y), dtype='float32')
correct = fluid.layers.reduce_sum(less)
return correct
class BowEncoder(object):
""" bow-encoder """
def __init__(self):
self.param_name = ""
def forward(self, emb):
return fluid.layers.sequence_pool(input=emb, pool_type='sum')
class GrnnEncoder(object):
""" grnn-encoder """
def __init__(self, param_name="grnn", hidden_size=128):
self.param_name = param_name
self.hidden_size = hidden_size
def forward(self, emb):
fc0 = fluid.layers.fc(input=emb,
size=self.hidden_size * 3,
param_attr=self.param_name + "_fc.w",
bias_attr=False)
gru_h = fluid.layers.dynamic_gru(
input=fc0,
size=self.hidden_size,
is_reverse=False,
param_attr=self.param_name + ".param",
bias_attr=self.param_name + ".bias")
return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
class PairwiseHingeLoss(object):
def __init__(self, margin=0.8):
self.margin = margin
def forward(self, pos, neg):
loss_part1 = fluid.layers.elementwise_sub(
tensor.fill_constant_batch_size_like(
input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
pos)
loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
loss_part3 = fluid.layers.elementwise_max(
tensor.fill_constant_batch_size_like(
input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_part2)
return loss_part3
models/recall/youtube_dnn/config.yaml
浏览文件 @ 2ff6b226
...@@ -13,37 +13,42 @@ ...@@ -13,37 +13,42 @@
# limitations under the License. # limitations under the License.
train: workspace: "paddlerec.models.recall.youtube_dnn"
trainer:
# for cluster training
strategy: "async"
epochs: 3 dataset:
workspace: "paddlerec.models.recall.youtube_dnn" - name: dataset_train
device: cpu batch_size: 5
type: DataLoader
#type: QueueDataset
data_path: "{workspace}/data/train"
data_converter: "{workspace}/random_reader.py"
hyper_parameters:
watch_vec_size: 64
search_vec_size: 64
other_feat_size: 64
output_size: 100
layers: [128, 64, 32]
optimizer:
class: adam
learning_rate: 0.001
strategy: async
reader: mode: train_runner
batch_size: 2
class: "{workspace}/random_reader.py"
train_data_path: "{workspace}/data/train"
model: runner:
models: "{workspace}/model.py" - name: train_runner
hyper_parameters: class: single_train
watch_vec_size: 64 device: cpu
search_vec_size: 64 epochs: 3
other_feat_size: 64 save_checkpoint_interval: 2
output_size: 100 save_inference_interval: 4
layers: [128, 64, 32] save_checkpoint_path: "increment"
learning_rate: 0.01 save_inference_path: "inference"
optimizer: sgd print_interval: 10
save: phase:
increment: - name: train
dirname: "increment" model: "{workspace}/model.py"
epoch_interval: 2 dataset_name: dataset_train
save_last: True thread_num: 1
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/recall/youtube_dnn/model.py
浏览文件 @ 2ff6b226
...@@ -13,39 +13,64 @@ ...@@ -13,39 +13,64 @@
# limitations under the License. # limitations under the License.
import math import math
import numpy as np
import paddle.fluid as fluid import paddle.fluid as fluid
from paddlerec.core.utils import envs from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase from paddlerec.core.model import Model as ModelBase
import numpy as np
class Model(ModelBase): class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def input_data(self, is_infer=False): def _init_hyper_parameters(self):
self.watch_vec_size = envs.get_global_env(
"hyper_parameters.watch_vec_size")
self.search_vec_size = envs.get_global_env(
"hyper_parameters.search_vec_size")
self.other_feat_size = envs.get_global_env(
"hyper_parameters.other_feat_size")
self.output_size = envs.get_global_env("hyper_parameters.output_size")
self.layers = envs.get_global_env("hyper_parameters.layers")
watch_vec_size = envs.get_global_env("hyper_parameters.watch_vec_size", def input_data(self, is_infer=False, **kwargs):
None, self._namespace)
search_vec_size = envs.get_global_env(
"hyper_parameters.search_vec_size", None, self._namespace)
other_feat_size = envs.get_global_env(
"hyper_parameters.other_feat_size", None, self._namespace)
watch_vec = fluid.data( watch_vec = fluid.data(
name="watch_vec", shape=[None, watch_vec_size], dtype="float32") name="watch_vec",
shape=[None, self.watch_vec_size],
dtype="float32")
search_vec = fluid.data( search_vec = fluid.data(
name="search_vec", shape=[None, search_vec_size], dtype="float32") name="search_vec",
shape=[None, self.search_vec_size],
dtype="float32")
other_feat = fluid.data( other_feat = fluid.data(
name="other_feat", shape=[None, other_feat_size], dtype="float32") name="other_feat",
shape=[None, self.other_feat_size],
dtype="float32")
label = fluid.data(name="label", shape=[None, 1], dtype="int64") label = fluid.data(name="label", shape=[None, 1], dtype="int64")
inputs = [watch_vec] + [search_vec] + [other_feat] + [label] inputs = [watch_vec] + [search_vec] + [other_feat] + [label]
self._data_var = inputs
return inputs return inputs
def fc(self, tag, data, out_dim, active='relu'): def net(self, inputs, is_infer=False):
concat_feats = fluid.layers.concat(input=inputs[:-1], axis=-1)
l1 = self._fc('l1', concat_feats, self.layers[0], 'relu')
l2 = self._fc('l2', l1, self.layers[1], 'relu')
l3 = self._fc('l3', l2, self.layers[2], 'relu')
l4 = self._fc('l4', l3, self.output_size, 'softmax')
num_seqs = fluid.layers.create_tensor(dtype='int64')
acc = fluid.layers.accuracy(input=l4, label=inputs[-1], total=num_seqs)
cost = fluid.layers.cross_entropy(input=l4, label=inputs[-1])
avg_cost = fluid.layers.mean(cost)
self._cost = avg_cost
self._metrics["acc"] = acc
def _fc(self, tag, data, out_dim, active='relu'):
init_stddev = 1.0 init_stddev = 1.0
scales = 1.0 / np.sqrt(data.shape[1]) scales = 1.0 / np.sqrt(data.shape[1])
...@@ -67,31 +92,3 @@ class Model(ModelBase): ...@@ -67,31 +92,3 @@ class Model(ModelBase):
bias_attr=b_attr, bias_attr=b_attr,
name=tag) name=tag)
return out return out
def net(self, inputs):
output_size = envs.get_global_env("hyper_parameters.output_size", None,
self._namespace)
layers = envs.get_global_env("hyper_parameters.layers", None,
self._namespace)
concat_feats = fluid.layers.concat(input=inputs[:-1], axis=-1)
l1 = self.fc('l1', concat_feats, layers[0], 'relu')
l2 = self.fc('l2', l1, layers[1], 'relu')
l3 = self.fc('l3', l2, layers[2], 'relu')
l4 = self.fc('l4', l3, output_size, 'softmax')
num_seqs = fluid.layers.create_tensor(dtype='int64')
acc = fluid.layers.accuracy(input=l4, label=inputs[-1], total=num_seqs)
cost = fluid.layers.cross_entropy(input=l4, label=inputs[-1])
avg_cost = fluid.layers.mean(cost)
self._cost = avg_cost
self._metrics["acc"] = acc
def train_net(self):
input_data = self.input_data()
self.net(input_data)
def infer_net(self):
pass
models/recall/youtube_dnn/random_reader.py
浏览文件 @ 2ff6b226
...@@ -13,22 +13,22 @@ ...@@ -13,22 +13,22 @@
# limitations under the License. # limitations under the License.
from __future__ import print_function from __future__ import print_function
import numpy as np
from paddlerec.core.reader import Reader from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs from paddlerec.core.utils import envs
from collections import defaultdict from collections import defaultdict
import numpy as np
class TrainReader(Reader): class TrainReader(Reader):
def init(self): def init(self):
self.watch_vec_size = envs.get_global_env( self.watch_vec_size = envs.get_global_env(
"hyper_parameters.watch_vec_size", None, "train.model") "hyper_parameters.watch_vec_size")
self.search_vec_size = envs.get_global_env( self.search_vec_size = envs.get_global_env(
"hyper_parameters.search_vec_size", None, "train.model") "hyper_parameters.search_vec_size")
self.other_feat_size = envs.get_global_env( self.other_feat_size = envs.get_global_env(
"hyper_parameters.other_feat_size", None, "train.model") "hyper_parameters.other_feat_size")
self.output_size = envs.get_global_env("hyper_parameters.output_size", self.output_size = envs.get_global_env("hyper_parameters.output_size")
None, "train.model")
def generate_sample(self, line): def generate_sample(self, line):
""" """
......
models/rerank/listwise/config.yaml
浏览文件 @ 2ff6b226
...@@ -12,44 +12,56 @@ ...@@ -12,44 +12,56 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
evaluate:
reader:
batch_size: 1
class: "{workspace}/random_infer_reader.py"
test_data_path: "{workspace}/data/train"
train: workspace: "paddlerec.models.rerank.listwise"
trainer:
# for cluster training
strategy: "async"
epochs: 3 dataset:
workspace: "paddlerec.models.rerank.listwise" - name: dataset_train
device: cpu type: DataLoader
data_path: "{workspace}/data/train"
data_converter: "{workspace}/random_reader.py"
- name: dataset_infer
type: DataLoader
data_path: "{workspace}/data/test"
data_converter: "{workspace}/random_reader.py"
reader: hyper_parameters:
batch_size: 2 hidden_size: 128
class: "{workspace}/random_reader.py" user_vocab: 200
train_data_path: "{workspace}/data/train" item_vocab: 1000
dataset_class: "DataLoader" item_len: 5
embed_size: 16
batch_size: 1
optimizer:
class: sgd
learning_rate: 0.01
strategy: async
model: #use infer_runner mode and modify 'phase' below if infer
models: "{workspace}/model.py" mode: train_runner
hyper_parameters: #mode: infer_runner
hidden_size: 128
user_vocab: 200 runner:
item_vocab: 1000 - name: train_runner
item_len: 5 class: single_train
embed_size: 16 device: cpu
learning_rate: 0.01 epochs: 3
optimizer: sgd save_checkpoint_interval: 2
save_inference_interval: 4
save_checkpoint_path: "increment"
save_inference_path: "inference"
- name: infer_runner
class: single_infer
init_model_path: "increment/0"
device: cpu
epochs: 3
save: phase:
increment: - name: train
dirname: "increment" model: "{workspace}/model.py"
epoch_interval: 2 dataset_name: dataset_train
save_last: True thread_num: 1
inference: #- name: infer
dirname: "inference" # model: "{workspace}/model.py"
epoch_interval: 4 # dataset_name: dataset_infer
save_last: True # thread_num: 1
models/rerank/listwise/model.py
浏览文件 @ 2ff6b226
...@@ -25,18 +25,13 @@ class Model(ModelBase): ...@@ -25,18 +25,13 @@ class Model(ModelBase):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def _init_hyper_parameters(self): def _init_hyper_parameters(self):
self.item_len = envs.get_global_env("hyper_parameters.self.item_len", self.item_len = envs.get_global_env("hyper_parameters.self.item_len")
None, self._namespace) self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size", self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
None, self._namespace) self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab", self.embed_size = envs.get_global_env("hyper_parameters.embed_size")
None, self._namespace)
self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab", def input_data(self, is_infer=False, **kwargs):
None, self._namespace)
self.embed_size = envs.get_global_env("hyper_parameters.embed_size",
None, self._namespace)
def input_data(self, is_infer=False):
user_slot_names = fluid.data( user_slot_names = fluid.data(
name='user_slot_names', name='user_slot_names',
shape=[None, 1], shape=[None, 1],
......
models/rerank/listwise/random_reader.py
浏览文件 @ 2ff6b226
...@@ -23,14 +23,10 @@ from collections import defaultdict ...@@ -23,14 +23,10 @@ from collections import defaultdict
class TrainReader(Reader): class TrainReader(Reader):
def init(self): def init(self):
self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab", self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
None, "train.model") self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab", self.item_len = envs.get_global_env("hyper_parameters.item_len")
None, "train.model") self.batch_size = envs.get_global_env("hyper_parameters.batch_size")
self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
"train.model")
self.batch_size = envs.get_global_env("batch_size", None,
"train.reader")
def reader_creator(self): def reader_creator(self):
def reader(): def reader():
......
models/rerank/readme.md
浏览文件 @ 2ff6b226
...@@ -9,9 +9,6 @@ ...@@ -9,9 +9,6 @@
* [整体介绍](#整体介绍) * [整体介绍](#整体介绍)
* [重排序模型列表](#重排序模型列表) * [重排序模型列表](#重排序模型列表)
* [使用教程](#使用教程) * [使用教程](#使用教程)
* [训练 预测](#训练 预测)
* [效果对比](#效果对比)
* [模型效果列表](#模型效果列表)
## 整体介绍 ## 整体介绍
### 融合模型列表 ### 融合模型列表
...@@ -29,15 +26,11 @@ ...@@ -29,15 +26,11 @@
<p> <p>
## 使用教程 ## 使用教程(快速开始)
### 训练 预测
```shell ```shell
python -m paddlerec.run -m paddlerec.models.rerank.listwise # listwise python -m paddlerec.run -m paddlerec.models.rerank.listwise # listwise
``` ```
## 效果对比 ## 使用教程（复现论文）
### 模型效果列表
| 数据集 | 模型 | loss | auc | listwise原论文没有给出训练数据，我们使用了随机的数据，可参考快速开始
| :------------------: | :--------------------: | :---------: |:---------: |
| -- | Listwise | -- | -- |
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