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PaddleRec
“e9549cbb78b01a570ded56b54cd1f5a0e5f44f12”上不存在“paddle/legacy/utils/tests/test_ThreadBarrier.cpp”
未验证 提交 88176619 编写于 作者: X xujiaqi01 提交者： GitHub
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models/multitask/esmm/config.yaml
浏览文件 @ 88176619
...@@ -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 @ 60d1b7fc
# 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 @ 88176619
...@@ -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 @ 88176619
...@@ -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 @ 60d1b7fc
# 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 @ 88176619
...@@ -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 @ 88176619
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/data/train/train_data.txt
浏览文件 @ 88176619
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1,0,9,0,0,0,0,1758.14,0,0,0,0,94,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0 1,0,9,0,0,0,0,1758.14,0,0,0,0,94,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0
......
models/multitask/mmoe/model.py
浏览文件 @ 88176619
...@@ -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 @ 88176619
...@@ -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 @ 60d1b7fc
# 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 @ 88176619
...@@ -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 @ 88176619
...@@ -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/rank/dcn/config.yaml
浏览文件 @ 88176619
...@@ -12,43 +12,66 @@ ...@@ -12,43 +12,66 @@
# 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.
train:
trainer: # global settings
# for cluster training debug: false
strategy: "async" workspace: "paddlerec.models.rank.dcn"
epochs: 10 dataset:
workspace: "paddlerec.models.rank.dcn" - name: train_sample
type: QueueDataset
reader: batch_size: 5
batch_size: 2 data_path: "{workspace}/data/sample_data/train"
train_data_path: "{workspace}/data/sample_data/train" sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
feat_dict_name: "{workspace}/data/vocab" dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/infer"
sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26" sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1" dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
model: hyper_parameters:
models: "{workspace}/model.py" optimizer:
hyper_parameters: class: Adam
cross_num: 2 learning_rate: 0.0001
dnn_hidden_units: [128, 128] # 用户自定义配置
l2_reg_cross: 0.00005 cross_num: 2
dnn_use_bn: False dnn_hidden_units: [128, 128]
clip_by_norm: 100.0 l2_reg_cross: 0.00005
cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt" dnn_use_bn: False
is_sparse: False clip_by_norm: 100.0
is_test: False cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt"
num_field: 39 is_sparse: False
learning_rate: 0.0001
act: "relu"
optimizer: adam mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
save:
increment: runner:
dirname: "increment" - name: train_runner
epoch_interval: 2 trainer_class: single_train
save_last: True epochs: 1
inference: device: cpu
dirname: "inference" init_model_path: ""
epoch_interval: 4 save_checkpoint_interval: 1
save_last: True save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: train_sample
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/dcn/data/sample_data/infer/infer_sample_data 0 → 100644
浏览文件 @ 88176619
label:0 I1:0.69314718056 I2:1.60943791243 I3:1.79175946923 I4:0.0 I5:7.23201033166 I6:1.60943791243 I7:2.77258872224 I8:1.09861228867 I9:5.20400668708 I10:0.69314718056 I11:1.09861228867 I12:0 I13:1.09861228867 C1:95 C2:398 C3:0 C4:0 C5:53 C6:1 C7:73 C8:71 C9:3 C10:1974 C11:832 C12:0 C13:875 C14:8 C15:1764 C16:0 C17:5 C18:390 C19:226 C20:1 C21:0 C22:0 C23:8 C24:1759 C25:1 C26:862
label:0 I1:1.09861228867 I2:1.38629436112 I3:3.80666248977 I4:0.69314718056 I5:4.63472898823 I6:2.19722457734 I7:1.09861228867 I8:1.09861228867 I9:1.60943791243 I10:0.69314718056 I11:0.69314718056 I12:0 I13:1.60943791243 C1:95 C2:200 C3:1184 C4:1929 C5:53 C6:4 C7:1477 C8:2 C9:3 C10:1283 C11:1567 C12:1048 C13:271 C14:6 C15:1551 C16:899 C17:1 C18:162 C19:226 C20:2 C21:575 C22:0 C23:8 C24:1615 C25:1 C26:659
label:0 I1:1.09861228867 I2:1.38629436112 I3:0.69314718056 I4:2.7080502011 I5:6.64378973315 I6:4.49980967033 I7:1.60943791243 I8:1.09861228867 I9:5.50533153593 I10:0.69314718056 I11:1.38629436112 I12:1.38629436112 I13:3.82864139649 C1:123 C2:378 C3:991 C4:197 C5:53 C6:1 C7:689 C8:2 C9:3 C10:245 C11:623 C12:1482 C13:887 C14:21 C15:106 C16:720 C17:3 C18:768 C19:0 C20:0 C21:1010 C22:1 C23:8 C24:720 C25:0 C26:0
label:0 I1:0 I2:6.79905586206 I3:0 I4:0 I5:8.38776764398 I6:0 I7:0.0 I8:0.0 I9:0.0 I10:0 I11:0.0 I12:0 I13:0 C1:95 C2:227 C3:0 C4:219 C5:53 C6:4 C7:3174 C8:2 C9:3 C10:569 C11:1963 C12:0 C13:1150 C14:21 C15:1656 C16:0 C17:6 C18:584 C19:0 C20:0 C21:0 C22:0 C23:8 C24:954 C25:0 C26:0
label:0 I1:1.38629436112 I2:1.09861228867 I3:0 I4:0.0 I5:1.09861228867 I6:0.0 I7:1.38629436112 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:121 C2:147 C3:0 C4:1356 C5:53 C6:7 C7:2120 C8:2 C9:3 C10:703 C11:1678 C12:1210 C13:1455 C14:8 C15:538 C16:1276 C17:6 C18:346 C19:0 C20:0 C21:944 C22:0 C23:10 C24:355 C25:0 C26:0
label:0 I1:0 I2:1.09861228867 I3:0 I4:0 I5:9.45915167004 I6:0 I7:0.0 I8:0.0 I9:1.94591014906 I10:0 I11:0.0 I12:0 I13:0 C1:14 C2:75 C3:993 C4:480 C5:50 C6:6 C7:1188 C8:2 C9:3 C10:245 C11:1037 C12:1365 C13:1421 C14:21 C15:786 C16:5 C17:2 C18:555 C19:0 C20:0 C21:1408 C22:6 C23:7 C24:753 C25:0 C26:0
label:0 I1:0 I2:1.60943791243 I3:1.09861228867 I4:0 I5:8.06117135969 I6:0 I7:0.0 I8:0.69314718056 I9:1.09861228867 I10:0 I11:0.0 I12:0 I13:0 C1:139 C2:343 C3:553 C4:828 C5:50 C6:4 C7:0 C8:2 C9:3 C10:245 C11:2081 C12:260 C13:455 C14:21 C15:122 C16:1159 C17:2 C18:612 C19:0 C20:0 C21:1137 C22:0 C23:1 C24:1583 C25:0 C26:0
label:1 I1:0.69314718056 I2:2.07944154168 I3:1.09861228867 I4:0.0 I5:0.0 I6:0.0 I7:0.69314718056 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:95 C2:227 C3:0 C4:1567 C5:21 C6:7 C7:2496 C8:71 C9:3 C10:1913 C11:2212 C12:0 C13:673 C14:21 C15:1656 C16:0 C17:5 C18:584 C19:0 C20:0 C21:0 C22:0 C23:10 C24:954 C25:0 C26:0
label:0 I1:0 I2:3.87120101091 I3:1.60943791243 I4:2.19722457734 I5:9.85277303799 I6:5.52146091786 I7:3.36729582999 I8:3.4657359028 I9:4.9558270576 I10:0 I11:0.69314718056 I12:0 I13:2.19722457734 C1:14 C2:14 C3:454 C4:197 C5:53 C6:1 C7:1386 C8:2 C9:3 C10:0 C11:1979 C12:205 C13:214 C14:6 C15:1837 C16:638 C17:5 C18:6 C19:0 C20:0 C21:70 C22:0 C23:10 C24:720 C25:0 C26:0
label:0 I1:0 I2:3.66356164613 I3:0 I4:0.69314718056 I5:10.4263800775 I6:3.09104245336 I7:0.69314718056 I8:1.09861228867 I9:1.38629436112 I10:0 I11:0.69314718056 I12:0 I13:0.69314718056 C1:14 C2:179 C3:120 C4:746 C5:53 C6:0 C7:1312 C8:2 C9:3 C10:1337 C11:1963 C12:905 C13:1150 C14:21 C15:1820 C16:328 C17:9 C18:77 C19:0 C20:0 C21:311 C22:0 C23:10 C24:89 C25:0 C26:0
models/rank/dcn/model.py
浏览文件 @ 88176619
...@@ -24,44 +24,21 @@ class Model(ModelBase): ...@@ -24,44 +24,21 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def init_network(self): def _init_hyper_parameters(self):
self.cross_num = envs.get_global_env("hyper_parameters.cross_num", self.cross_num = envs.get_global_env("hyper_parameters.cross_num",
None, self._namespace) None)
self.dnn_hidden_units = envs.get_global_env( self.dnn_hidden_units = envs.get_global_env(
"hyper_parameters.dnn_hidden_units", None, self._namespace) "hyper_parameters.dnn_hidden_units", None)
self.l2_reg_cross = envs.get_global_env( self.l2_reg_cross = envs.get_global_env(
"hyper_parameters.l2_reg_cross", None, self._namespace) "hyper_parameters.l2_reg_cross", None)
self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn", self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn",
None, self._namespace) None)
self.clip_by_norm = envs.get_global_env( self.clip_by_norm = envs.get_global_env(
"hyper_parameters.clip_by_norm", None, self._namespace) "hyper_parameters.clip_by_norm", None)
cat_feat_num = envs.get_global_env("hyper_parameters.cat_feat_num", self.cat_feat_num = envs.get_global_env(
None, self._namespace) "hyper_parameters.cat_feat_num", None)
self.sparse_inputs = self._sparse_data_var[1:]
self.dense_inputs = self._dense_data_var
self.target_input = self._sparse_data_var[0]
cat_feat_dims_dict = OrderedDict()
for line in open(cat_feat_num):
spls = line.strip().split()
assert len(spls) == 2
cat_feat_dims_dict[spls[0]] = int(spls[1])
self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict(
)
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
None, self._namespace) None)
self.dense_feat_names = [i.name for i in self.dense_inputs]
self.sparse_feat_names = [i.name for i in self.sparse_inputs]
# {feat_name: dims}
self.feat_dims_dict = OrderedDict(
[(feat_name, 1) for feat_name in self.dense_feat_names])
self.feat_dims_dict.update(self.cat_feat_dims_dict)
self.net_input = None
self.loss = None
def _create_embedding_input(self): def _create_embedding_input(self):
# sparse embedding # sparse embedding
...@@ -121,9 +98,29 @@ class Model(ModelBase): ...@@ -121,9 +98,29 @@ class Model(ModelBase):
def _l2_loss(self, w): def _l2_loss(self, w):
return fluid.layers.reduce_sum(fluid.layers.square(w)) return fluid.layers.reduce_sum(fluid.layers.square(w))
def train_net(self): def net(self, inputs, is_infer=False):
self._init_slots() self.sparse_inputs = self._sparse_data_var[1:]
self.init_network() self.dense_inputs = self._dense_data_var
self.target_input = self._sparse_data_var[0]
cat_feat_dims_dict = OrderedDict()
for line in open(self.cat_feat_num):
spls = line.strip().split()
assert len(spls) == 2
cat_feat_dims_dict[spls[0]] = int(spls[1])
self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict(
)
self.dense_feat_names = [i.name for i in self.dense_inputs]
self.sparse_feat_names = [i.name for i in self.sparse_inputs]
# {feat_name: dims}
self.feat_dims_dict = OrderedDict(
[(feat_name, 1) for feat_name in self.dense_feat_names])
self.feat_dims_dict.update(self.cat_feat_dims_dict)
self.net_input = None
self.loss = None
self.net_input = self._create_embedding_input() self.net_input = self._create_embedding_input()
...@@ -146,6 +143,9 @@ class Model(ModelBase): ...@@ -146,6 +143,9 @@ class Model(ModelBase):
self._metrics["AUC"] = auc_var self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var
# logloss # logloss
logloss = fluid.layers.log_loss( logloss = fluid.layers.log_loss(
self.prob, fluid.layers.cast( self.prob, fluid.layers.cast(
...@@ -157,11 +157,7 @@ class Model(ModelBase): ...@@ -157,11 +157,7 @@ class Model(ModelBase):
self.loss = self.avg_logloss + l2_reg_cross_loss self.loss = self.avg_logloss + l2_reg_cross_loss
self._cost = self.loss self._cost = self.loss
def optimizer(self): #def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate", #
None, self._namespace) # optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)
optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True) # return optimizer
return optimizer
def infer_net(self):
self.train_net()
models/rank/deepfm/config.yaml
浏览文件 @ 88176619
...@@ -12,39 +12,65 @@ ...@@ -12,39 +12,65 @@
# 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.
train: # global settings
trainer: debug: false
# for cluster training workspace: "paddlerec.models.rank.deepfm"
strategy: "async"
epochs: 10 dataset:
workspace: "paddlerec.models.rank.deepfm" - name: train_sample
type: QueueDataset
reader: batch_size: 5
batch_size: 2 data_path: "{workspace}/data/sample_data/train"
train_data_path: "{workspace}/data/sample_data/train" sparse_slots: "label feat_idx"
feat_dict_name: "{workspace}/data/sample_data/feat_dict_10.pkl2" dense_slots: "feat_value:39"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label feat_idx" sparse_slots: "label feat_idx"
dense_slots: "feat_value:39" dense_slots: "feat_value:39"
model: hyper_parameters:
models: "{workspace}/model.py" optimizer:
hyper_parameters: class: SGD
sparse_feature_number: 1086460 learning_rate: 0.0001
sparse_feature_dim: 9 sparse_feature_number: 1086460
num_field: 39 sparse_feature_dim: 9
fc_sizes: [400, 400, 400] num_field: 39
learning_rate: 0.0001 fc_sizes: [400, 400, 400]
reg: 0.001 reg: 0.001
act: "relu" act: "relu"
optimizer: SGD
save: mode: train_runner
increment: # if infer, change mode to "infer_runner" and change phase to "infer_phase"
dirname: "increment"
epoch_interval: 2 runner:
save_last: True - name: train_runner
inference: trainer_class: single_train
dirname: "inference" epochs: 2
epoch_interval: 4 device: cpu
save_last: True init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: train_sample
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/deepfm/model.py
浏览文件 @ 88176619
...@@ -24,42 +24,46 @@ class Model(ModelBase): ...@@ -24,42 +24,46 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def deepfm_net(self): def _init_hyper_parameters(self):
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes",
None)
self.act = envs.get_global_env("hyper_parameters.act", None)
def net(self, inputs, is_infer=False):
init_value_ = 0.1 init_value_ = 0.1
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
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)
# ------------------------- network input -------------------------- # ------------------------- network input --------------------------
num_field = envs.get_global_env("hyper_parameters.num_field", None,
self._namespace)
raw_feat_idx = self._sparse_data_var[1] raw_feat_idx = self._sparse_data_var[1]
raw_feat_value = self._dense_data_var[0] raw_feat_value = self._dense_data_var[0]
self.label = self._sparse_data_var[0] self.label = self._sparse_data_var[0]
feat_idx = raw_feat_idx feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape( feat_value = fluid.layers.reshape(
raw_feat_value, [-1, num_field, 1]) # None * num_field * 1 raw_feat_value, [-1, self.num_field, 1]) # None * num_field * 1
reg = envs.get_global_env("hyper_parameters.reg", 1e-4,
self._namespace)
first_weights_re = fluid.embedding( first_weights_re = fluid.embedding(
input=feat_idx, input=feat_idx,
is_sparse=True, is_sparse=True,
is_distributed=is_distributed, is_distributed=is_distributed,
dtype='float32', dtype='float32',
size=[sparse_feature_number + 1, 1], size=[self.sparse_feature_number + 1, 1],
padding_idx=0, padding_idx=0,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer( initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_), loc=0.0, scale=init_value_),
regularizer=fluid.regularizer.L1DecayRegularizer(reg))) regularizer=fluid.regularizer.L1DecayRegularizer(self.reg)))
first_weights = fluid.layers.reshape( first_weights = fluid.layers.reshape(
first_weights_re, shape=[-1, num_field, 1]) # None * num_field * 1 first_weights_re,
shape=[-1, self.num_field, 1]) # None * num_field * 1
y_first_order = fluid.layers.reduce_sum((first_weights * feat_value), y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
1) 1)
...@@ -70,16 +74,17 @@ class Model(ModelBase): ...@@ -70,16 +74,17 @@ class Model(ModelBase):
is_sparse=True, is_sparse=True,
is_distributed=is_distributed, is_distributed=is_distributed,
dtype='float32', dtype='float32',
size=[sparse_feature_number + 1, sparse_feature_dim], size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0, padding_idx=0,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer( initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, loc=0.0,
scale=init_value_ / math.sqrt(float(sparse_feature_dim))))) scale=init_value_ /
math.sqrt(float(self.sparse_feature_dim)))))
feat_embeddings = fluid.layers.reshape( feat_embeddings = fluid.layers.reshape(
feat_embeddings_re, feat_embeddings_re,
shape=[-1, num_field, shape=[-1, self.num_field, self.sparse_feature_dim
sparse_feature_dim]) # None * num_field * embedding_size ]) # None * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size
# sum_square part # sum_square part
...@@ -101,17 +106,13 @@ class Model(ModelBase): ...@@ -101,17 +106,13 @@ class Model(ModelBase):
# ------------------------- DNN -------------------------- # ------------------------- DNN --------------------------
layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None, y_dnn = fluid.layers.reshape(
self._namespace) feat_embeddings, [-1, self.num_field * self.sparse_feature_dim])
act = envs.get_global_env("hyper_parameters.act", None, for s in self.layer_sizes:
self._namespace)
y_dnn = fluid.layers.reshape(feat_embeddings,
[-1, num_field * sparse_feature_dim])
for s in layer_sizes:
y_dnn = fluid.layers.fc( y_dnn = fluid.layers.fc(
input=y_dnn, input=y_dnn,
size=s, size=s,
act=act, act=self.act,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer( initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_ / math.sqrt(float(10)))), loc=0.0, scale=init_value_ / math.sqrt(float(10)))),
...@@ -133,21 +134,12 @@ class Model(ModelBase): ...@@ -133,21 +134,12 @@ class Model(ModelBase):
self.predict = fluid.layers.sigmoid(y_first_order + y_second_order + self.predict = fluid.layers.sigmoid(y_first_order + y_second_order +
y_dnn) y_dnn)
def train_net(self):
self._init_slots()
self.deepfm_net()
# ------------------------- Cost(logloss) --------------------------
cost = fluid.layers.log_loss( cost = fluid.layers.log_loss(
input=self.predict, label=fluid.layers.cast(self.label, "float32")) input=self.predict, label=fluid.layers.cast(self.label, "float32"))
avg_cost = fluid.layers.reduce_sum(cost) avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost self._cost = avg_cost
# ------------------------- Metric(Auc) --------------------------
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1) predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64') label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d, auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
...@@ -155,12 +147,5 @@ class Model(ModelBase): ...@@ -155,12 +147,5 @@ class Model(ModelBase):
slide_steps=0) slide_steps=0)
self._metrics["AUC"] = auc_var self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
def optimizer(self): self._infer_results["AUC"] = auc_var
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
return optimizer
def infer_net(self):
self.train_net()
models/rank/din/config.yaml
浏览文件 @ 88176619
...@@ -12,40 +12,60 @@ ...@@ -12,40 +12,60 @@
# 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.
train: # global settings
trainer: debug: false
# for cluster training workspace: "paddlerec.models.rank.din"
strategy: "async"
epochs: 10 dataset:
workspace: "paddlerec.models.rank.din" - name: sample_1
type: DataLoader
batch_size: 5
data_path: "{workspace}/data/train_data"
data_converter: "{workspace}/reader.py"
- name: infer_sample
type: DataLoader
batch_size: 5
data_path: "{workspace}/data/train_data"
data_converter: "{workspace}/reader.py"
reader: hyper_parameters:
batch_size: 2 optimizer:
class: "{workspace}/reader.py" class: SGD
train_data_path: "{workspace}/data/train_data" learning_rate: 0.0001
dataset_class: "DataLoader" use_DataLoader: True
item_emb_size: 64
cat_emb_size: 64
is_sparse: False
item_count: 63001
cat_count: 801
model: act: "sigmoid"
models: "{workspace}/model.py"
hyper_parameters:
use_DataLoader: True
item_emb_size: 64
cat_emb_size: 64
is_sparse: False
config_path: "data/config.txt"
fc_sizes: [400, 400, 400]
learning_rate: 0.0001
reg: 0.001
act: "sigmoid"
optimizer: SGD
save:
increment: mode: train_runner
dirname: "increment"
epoch_interval: 2 runner:
save_last: True - name: train_runner
inference: trainer_class: single_train
dirname: "inference" epochs: 1
epoch_interval: 4 device: cpu
save_last: True init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: sample_1
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/din/model.py
浏览文件 @ 88176619
...@@ -22,12 +22,58 @@ class Model(ModelBase): ...@@ -22,12 +22,58 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def config_read(self, config_path): def _init_hyper_parameters(self):
with open(config_path, "r") as fin: self.item_emb_size = envs.get_global_env(
user_count = int(fin.readline().strip()) "hyper_parameters.item_emb_size", 64)
item_count = int(fin.readline().strip()) self.cat_emb_size = envs.get_global_env(
cat_count = int(fin.readline().strip()) "hyper_parameters.cat_emb_size", 64)
return user_count, item_count, cat_count self.act = envs.get_global_env("hyper_parameters.act", "sigmoid")
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
False)
#significant for speeding up the training process
self.use_DataLoader = envs.get_global_env(
"hyper_parameters.use_DataLoader", False)
self.item_count = envs.get_global_env("hyper_parameters.item_count",
63001)
self.cat_count = envs.get_global_env("hyper_parameters.cat_count", 801)
def input_data(self, is_infer=False, **kwargs):
seq_len = -1
self.data_var = []
hist_item_seq = fluid.data(
name="hist_item_seq", shape=[None, seq_len], dtype="int64")
self.data_var.append(hist_item_seq)
hist_cat_seq = fluid.data(
name="hist_cat_seq", shape=[None, seq_len], dtype="int64")
self.data_var.append(hist_cat_seq)
target_item = fluid.data(
name="target_item", shape=[None], dtype="int64")
self.data_var.append(target_item)
target_cat = fluid.data(name="target_cat", shape=[None], dtype="int64")
self.data_var.append(target_cat)
label = fluid.data(name="label", shape=[None, 1], dtype="float32")
self.data_var.append(label)
mask = fluid.data(
name="mask", shape=[None, seq_len, 1], dtype="float32")
self.data_var.append(mask)
target_item_seq = fluid.data(
name="target_item_seq", shape=[None, seq_len], dtype="int64")
self.data_var.append(target_item_seq)
target_cat_seq = fluid.data(
name="target_cat_seq", shape=[None, seq_len], dtype="int64")
self.data_var.append(target_cat_seq)
train_inputs = [hist_item_seq] + [hist_cat_seq] + [target_item] + [
target_cat
] + [label] + [mask] + [target_item_seq] + [target_cat_seq]
return train_inputs
def din_attention(self, hist, target_expand, mask): def din_attention(self, hist, target_expand, mask):
"""activation weight""" """activation weight"""
...@@ -59,104 +105,58 @@ class Model(ModelBase): ...@@ -59,104 +105,58 @@ class Model(ModelBase):
out = fluid.layers.reshape(x=out, shape=[0, hidden_size]) out = fluid.layers.reshape(x=out, shape=[0, hidden_size])
return out return out
def train_net(self): def net(self, inputs, is_infer=False):
seq_len = -1 hist_item_seq = inputs[0]
self.item_emb_size = envs.get_global_env( hist_cat_seq = inputs[1]
"hyper_parameters.item_emb_size", 64, self._namespace) target_item = inputs[2]
self.cat_emb_size = envs.get_global_env( target_cat = inputs[3]
"hyper_parameters.cat_emb_size", 64, self._namespace) label = inputs[4]
self.act = envs.get_global_env("hyper_parameters.act", "sigmoid", mask = inputs[5]
self._namespace) target_item_seq = inputs[6]
#item_emb_size = 64 target_cat_seq = inputs[7]
#cat_emb_size = 64
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
False, self._namespace)
#significant for speeding up the training process
self.config_path = envs.get_global_env(
"hyper_parameters.config_path", "data/config.txt", self._namespace)
self.use_DataLoader = envs.get_global_env(
"hyper_parameters.use_DataLoader", False, self._namespace)
user_count, item_count, cat_count = self.config_read(self.config_path)
item_emb_attr = fluid.ParamAttr(name="item_emb") item_emb_attr = fluid.ParamAttr(name="item_emb")
cat_emb_attr = fluid.ParamAttr(name="cat_emb") cat_emb_attr = fluid.ParamAttr(name="cat_emb")
hist_item_seq = fluid.data(
name="hist_item_seq", shape=[None, seq_len], dtype="int64")
self._data_var.append(hist_item_seq)
hist_cat_seq = fluid.data(
name="hist_cat_seq", shape=[None, seq_len], dtype="int64")
self._data_var.append(hist_cat_seq)
target_item = fluid.data(
name="target_item", shape=[None], dtype="int64")
self._data_var.append(target_item)
target_cat = fluid.data(name="target_cat", shape=[None], dtype="int64")
self._data_var.append(target_cat)
label = fluid.data(name="label", shape=[None, 1], dtype="float32")
self._data_var.append(label)
mask = fluid.data(
name="mask", shape=[None, seq_len, 1], dtype="float32")
self._data_var.append(mask)
target_item_seq = fluid.data(
name="target_item_seq", shape=[None, seq_len], dtype="int64")
self._data_var.append(target_item_seq)
target_cat_seq = fluid.data(
name="target_cat_seq", shape=[None, seq_len], dtype="int64")
self._data_var.append(target_cat_seq)
if self.use_DataLoader:
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=10000,
use_double_buffer=False,
iterable=False)
hist_item_emb = fluid.embedding( hist_item_emb = fluid.embedding(
input=hist_item_seq, input=hist_item_seq,
size=[item_count, self.item_emb_size], size=[self.item_count, self.item_emb_size],
param_attr=item_emb_attr, param_attr=item_emb_attr,
is_sparse=self.is_sparse) is_sparse=self.is_sparse)
hist_cat_emb = fluid.embedding( hist_cat_emb = fluid.embedding(
input=hist_cat_seq, input=hist_cat_seq,
size=[cat_count, self.cat_emb_size], size=[self.cat_count, self.cat_emb_size],
param_attr=cat_emb_attr, param_attr=cat_emb_attr,
is_sparse=self.is_sparse) is_sparse=self.is_sparse)
target_item_emb = fluid.embedding( target_item_emb = fluid.embedding(
input=target_item, input=target_item,
size=[item_count, self.item_emb_size], size=[self.item_count, self.item_emb_size],
param_attr=item_emb_attr, param_attr=item_emb_attr,
is_sparse=self.is_sparse) is_sparse=self.is_sparse)
target_cat_emb = fluid.embedding( target_cat_emb = fluid.embedding(
input=target_cat, input=target_cat,
size=[cat_count, self.cat_emb_size], size=[self.cat_count, self.cat_emb_size],
param_attr=cat_emb_attr, param_attr=cat_emb_attr,
is_sparse=self.is_sparse) is_sparse=self.is_sparse)
target_item_seq_emb = fluid.embedding( target_item_seq_emb = fluid.embedding(
input=target_item_seq, input=target_item_seq,
size=[item_count, self.item_emb_size], size=[self.item_count, self.item_emb_size],
param_attr=item_emb_attr, param_attr=item_emb_attr,
is_sparse=self.is_sparse) is_sparse=self.is_sparse)
target_cat_seq_emb = fluid.embedding( target_cat_seq_emb = fluid.embedding(
input=target_cat_seq, input=target_cat_seq,
size=[cat_count, self.cat_emb_size], size=[self.cat_count, self.cat_emb_size],
param_attr=cat_emb_attr, param_attr=cat_emb_attr,
is_sparse=self.is_sparse) is_sparse=self.is_sparse)
item_b = fluid.embedding( item_b = fluid.embedding(
input=target_item, input=target_item,
size=[item_count, 1], size=[self.item_count, 1],
param_attr=fluid.initializer.Constant(value=0.0)) param_attr=fluid.initializer.Constant(value=0.0))
hist_seq_concat = fluid.layers.concat( hist_seq_concat = fluid.layers.concat(
...@@ -195,12 +195,5 @@ class Model(ModelBase): ...@@ -195,12 +195,5 @@ class Model(ModelBase):
slide_steps=0) slide_steps=0)
self._metrics["AUC"] = auc_var self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
def optimizer(self): self._infer_results["AUC"] = auc_var
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
return optimizer
def infer_net(self, parameter_list):
self.deepfm_net()
models/rank/din/reader.py
浏览文件 @ 88176619
...@@ -29,8 +29,8 @@ from paddlerec.core.utils import envs ...@@ -29,8 +29,8 @@ from paddlerec.core.utils import envs
class TrainReader(Reader): class TrainReader(Reader):
def init(self): def init(self):
self.train_data_path = envs.get_global_env("train_data_path", None, self.train_data_path = envs.get_global_env(
"train.reader") "dataset.sample_1.data_path", None)
self.res = [] self.res = []
self.max_len = 0 self.max_len = 0
...@@ -46,7 +46,8 @@ class TrainReader(Reader): ...@@ -46,7 +46,8 @@ class TrainReader(Reader):
fo = open("tmp.txt", "w") fo = open("tmp.txt", "w")
fo.write(str(self.max_len)) fo.write(str(self.max_len))
fo.close() fo.close()
self.batch_size = envs.get_global_env("batch_size", 32, "train.reader") self.batch_size = envs.get_global_env("dataset.sample_1.batch_size",
32, "train.reader")
self.group_size = self.batch_size * 20 self.group_size = self.batch_size * 20
def _process_line(self, line): def _process_line(self, line):
......
models/rank/readme.md
浏览文件 @ 88176619
...@@ -56,7 +56,18 @@ ...@@ -56,7 +56,18 @@
<img align="center" src="../../doc/imgs/din.png"> <img align="center" src="../../doc/imgs/din.png">
<p> <p>
## 使用教程 ## 使用教程(快速开始)
使用样例数据快速开始，参考[训练](###训练) & [预测](###预测)
## 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 |
| :------------------: | :--------------------: | :--------------------: | :--------------------: |
| DNN | 1000 | 10 | 1 |
| DCN | 512 | 20 | 2 |
| DeepFM | 100 | 10 | 30 |
| DIN | 32 | 10 | 100 |
| Wide&Deep | 40 | 1 | 40 |
| xDeepFM | 100 | 1 | 10 |
### 数据处理 ### 数据处理
参考每个模型目录数据下载&预处理脚本 参考每个模型目录数据下载&预处理脚本
...@@ -68,11 +79,21 @@ sh run.sh ...@@ -68,11 +79,21 @@ sh run.sh
### 训练 ### 训练
``` ```
python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例 cd modles/rank/dnn # 进入选定好的排序模型的目录 以DNN为例
python -m paddlerec.run -m paddlerec.models.rank.dnn # 使用内置配置
# 如果需要使用自定义配置，config.yaml中workspace需要使用改模型目录的绝对路径
# 自定义修改超参后，指定配置文件，使用自定义配置
python -m paddlerec.run -m ./config.yaml
``` ```
### 预测 ### 预测
``` ```
python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例 # 修改对应模型的config.yaml，mode配置infer_runner
# 示例: mode: runner1 -> mode: infer_runner
# infer_runner中 class配置为 class: single_infer
# 如果训练阶段和预测阶段的模型输入一致，phase不需要改动，复用train的即可
# 修改完config.yaml后 执行:
python -m paddlerec.run -m ./config.yaml # 以DNN为例
``` ```
## 效果对比 ## 效果对比
...@@ -87,6 +108,7 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例 ...@@ -87,6 +108,7 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例
| Census-income Data | Wide&Deep | 0.76195 | 0.90577 | -- | -- | | Census-income Data | Wide&Deep | 0.76195 | 0.90577 | -- | -- |
| Amazon Product | DIN | 0.47005 | 0.86379 | -- | -- | | Amazon Product | DIN | 0.47005 | 0.86379 | -- | -- |
## 分布式 ## 分布式
### 模型训练性能 (样本/s) ### 模型训练性能 (样本/s)
| 数据集 | 模型 | 单机 | 同步 (4节点) | 同步 (8节点) | 同步 (16节点) | 同步 (32节点) | | 数据集 | 模型 | 单机 | 同步 (4节点) | 同步 (8节点) | 同步 (16节点) | 同步 (32节点) |
......
models/rank/wide_deep/config.yaml
浏览文件 @ 88176619
...@@ -12,37 +12,59 @@ ...@@ -12,37 +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.
train: # global settings
trainer: debug: false
# for cluster training workspace: "paddlerec.models.rank.wide_deep"
strategy: "async"
epochs: 10
workspace: "paddlerec.models.rank.wide_deep"
reader: dataset:
batch_size: 2 - name: sample_1
train_data_path: "{workspace}/data/sample_data/train" type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label" sparse_slots: "label"
dense_slots: "wide_input:8 deep_input:58" dense_slots: "wide_input:8 deep_input:58"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label"
dense_slots: "wide_input:8 deep_input:58"
hyper_parameters:
optimizer:
class: SGD
learning_rate: 0.0001
hidden1_units: 75
hidden2_units: 50
hidden3_units: 25
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
- name: train_runner
trainer_class: single_train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
model: phase:
models: "{workspace}/model.py" - name: phase1
hyper_parameters: model: "{workspace}/model.py"
hidden1_units: 75 dataset_name: sample_1
hidden2_units: 50 thread_num: 1
hidden3_units: 25 #- name: infer_phase
learning_rate: 0.0001 # model: "{workspace}/model.py"
reg: 0.001 # dataset_name: infer_sample
act: "relu" # thread_num: 1
optimizer: SGD
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/rank/wide_deep/model.py
浏览文件 @ 88176619
...@@ -24,6 +24,14 @@ class Model(ModelBase): ...@@ -24,6 +24,14 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.hidden1_units = envs.get_global_env(
"hyper_parameters.hidden1_units", 75)
self.hidden2_units = envs.get_global_env(
"hyper_parameters.hidden2_units", 50)
self.hidden3_units = envs.get_global_env(
"hyper_parameters.hidden3_units", 25)
def wide_part(self, data): def wide_part(self, data):
out = fluid.layers.fc( out = fluid.layers.fc(
input=data, input=data,
...@@ -56,21 +64,14 @@ class Model(ModelBase): ...@@ -56,21 +64,14 @@ class Model(ModelBase):
return l3 return l3
def train_net(self): def net(self, inputs, is_infer=False):
self._init_slots()
wide_input = self._dense_data_var[0] wide_input = self._dense_data_var[0]
deep_input = self._dense_data_var[1] deep_input = self._dense_data_var[1]
label = self._sparse_data_var[0] label = self._sparse_data_var[0]
hidden1_units = envs.get_global_env("hyper_parameters.hidden1_units",
75, self._namespace)
hidden2_units = envs.get_global_env("hyper_parameters.hidden2_units",
50, self._namespace)
hidden3_units = envs.get_global_env("hyper_parameters.hidden3_units",
25, self._namespace)
wide_output = self.wide_part(wide_input) wide_output = self.wide_part(wide_input)
deep_output = self.deep_part(deep_input, hidden1_units, hidden2_units, deep_output = self.deep_part(deep_input, self.hidden1_units,
hidden3_units) self.hidden2_units, self.hidden3_units)
wide_model = fluid.layers.fc( wide_model = fluid.layers.fc(
input=wide_output, input=wide_output,
...@@ -109,18 +110,12 @@ class Model(ModelBase): ...@@ -109,18 +110,12 @@ class Model(ModelBase):
self._metrics["AUC"] = auc_var self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc self._metrics["BATCH_AUC"] = batch_auc
self._metrics["ACC"] = acc self._metrics["ACC"] = acc
if is_infer:
self._infer_results["AUC"] = auc_var
self._infer_results["ACC"] = acc
cost = fluid.layers.sigmoid_cross_entropy_with_logits( cost = fluid.layers.sigmoid_cross_entropy_with_logits(
x=prediction, label=fluid.layers.cast( x=prediction, label=fluid.layers.cast(
label, dtype='float32')) label, dtype='float32'))
avg_cost = fluid.layers.mean(cost) avg_cost = fluid.layers.mean(cost)
self._cost = avg_cost self._cost = avg_cost
def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
return optimizer
def infer_net(self):
self.train_net()
models/rank/xdeepfm/config.yaml
浏览文件 @ 88176619
...@@ -11,41 +11,61 @@ ...@@ -11,41 +11,61 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# 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.
debug: false
workspace: "paddlerec.models.rank.xdeepfm"
train: dataset:
trainer: - name: sample_1
# for cluster training type: QueueDataset #或者DataLoader
strategy: "async" batch_size: 5
data_path: "{workspace}/data/sample_data/train"
epochs: 10 sparse_slots: "label feat_idx"
workspace: "paddlerec.models.rank.xdeepfm" dense_slots: "feat_value:39"
- name: infer_sample
reader: type: QueueDataset #或者DataLoader
batch_size: 2 batch_size: 5
train_data_path: "{workspace}/data/sample_data/train" data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label feat_idx" sparse_slots: "label feat_idx"
dense_slots: "feat_value:39" dense_slots: "feat_value:39"
model: hyper_parameters:
models: "{workspace}/model.py" optimizer:
hyper_parameters: class: SGD
layer_sizes_dnn: [10, 10, 10] learning_rate: 0.0001
layer_sizes_cin: [10, 10] layer_sizes_dnn: [10, 10, 10]
sparse_feature_number: 1086460 layer_sizes_cin: [10, 10]
sparse_feature_dim: 9 sparse_feature_number: 1086460
num_field: 39 sparse_feature_dim: 9
fc_sizes: [400, 400, 400] num_field: 39
learning_rate: 0.0001 fc_sizes: [400, 400, 400]
reg: 0.0001 act: "relu"
act: "relu"
optimizer: SGD
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
- name: train_runner
trainer_class: single_train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
save: phase:
increment: - name: phase1
dirname: "increment" model: "{workspace}/model.py"
epoch_interval: 2 dataset_name: sample_1
save_last: True thread_num: 1
inference: #- name: infer_phase
dirname: "inference" # model: "{workspace}/model.py"
epoch_interval: 4 # dataset_name: infer_sample
save_last: True # thread_num: 1
models/rank/xdeepfm/model.py
浏览文件 @ 88176619
...@@ -22,38 +22,45 @@ class Model(ModelBase): ...@@ -22,38 +22,45 @@ class Model(ModelBase):
def __init__(self, config): def __init__(self, config):
ModelBase.__init__(self, config) ModelBase.__init__(self, config)
def xdeepfm_net(self): def _init_hyper_parameters(self):
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
self.layer_sizes_cin = envs.get_global_env(
"hyper_parameters.layer_sizes_cin", None)
self.layer_sizes_dnn = envs.get_global_env(
"hyper_parameters.layer_sizes_dnn", None)
self.act = envs.get_global_env("hyper_parameters.act", None)
def net(self, inputs, is_infer=False):
raw_feat_idx = self._sparse_data_var[1]
raw_feat_value = self._dense_data_var[0]
self.label = self._sparse_data_var[0]
init_value_ = 0.1 init_value_ = 0.1
initer = fluid.initializer.TruncatedNormalInitializer( initer = fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_) loc=0.0, scale=init_value_)
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
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)
# ------------------------- network input -------------------------- # ------------------------- network input --------------------------
num_field = envs.get_global_env("hyper_parameters.num_field", None,
self._namespace)
raw_feat_idx = self._sparse_data_var[1]
raw_feat_value = self._dense_data_var[0]
self.label = self._sparse_data_var[0]
feat_idx = raw_feat_idx feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape( feat_value = fluid.layers.reshape(
raw_feat_value, [-1, num_field, 1]) # None * num_field * 1 raw_feat_value, [-1, self.num_field, 1]) # None * num_field * 1
feat_embeddings = fluid.embedding( feat_embeddings = fluid.embedding(
input=feat_idx, input=feat_idx,
is_sparse=True, is_sparse=True,
dtype='float32', dtype='float32',
size=[sparse_feature_number + 1, sparse_feature_dim], size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0, padding_idx=0,
param_attr=fluid.ParamAttr(initializer=initer)) param_attr=fluid.ParamAttr(initializer=initer))
feat_embeddings = fluid.layers.reshape(feat_embeddings, [ feat_embeddings = fluid.layers.reshape(feat_embeddings, [
-1, num_field, sparse_feature_dim -1, self.num_field, self.sparse_feature_dim
]) # None * num_field * embedding_size ]) # None * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size
...@@ -63,11 +70,11 @@ class Model(ModelBase): ...@@ -63,11 +70,11 @@ class Model(ModelBase):
input=feat_idx, input=feat_idx,
is_sparse=True, is_sparse=True,
dtype='float32', dtype='float32',
size=[sparse_feature_number + 1, 1], size=[self.sparse_feature_number + 1, 1],
padding_idx=0, padding_idx=0,
param_attr=fluid.ParamAttr(initializer=initer)) param_attr=fluid.ParamAttr(initializer=initer))
weights_linear = fluid.layers.reshape( weights_linear = fluid.layers.reshape(
weights_linear, [-1, num_field, 1]) # None * num_field * 1 weights_linear, [-1, self.num_field, 1]) # None * num_field * 1
b_linear = fluid.layers.create_parameter( b_linear = fluid.layers.create_parameter(
shape=[1], shape=[1],
dtype='float32', dtype='float32',
...@@ -77,31 +84,30 @@ class Model(ModelBase): ...@@ -77,31 +84,30 @@ class Model(ModelBase):
# -------------------- CIN -------------------- # -------------------- CIN --------------------
layer_sizes_cin = envs.get_global_env(
"hyper_parameters.layer_sizes_cin", None, self._namespace)
Xs = [feat_embeddings] Xs = [feat_embeddings]
last_s = num_field last_s = self.num_field
for s in layer_sizes_cin: for s in self.layer_sizes_cin:
# calculate Z^(k+1) with X^k and X^0 # calculate Z^(k+1) with X^k and X^0
X_0 = fluid.layers.reshape( X_0 = fluid.layers.reshape(
fluid.layers.transpose(Xs[0], [0, 2, 1]), fluid.layers.transpose(Xs[0], [0, 2, 1]),
[-1, sparse_feature_dim, num_field, [-1, self.sparse_feature_dim, self.num_field,
1]) # None, embedding_size, num_field, 1 1]) # None, embedding_size, num_field, 1
X_k = fluid.layers.reshape( X_k = fluid.layers.reshape(
fluid.layers.transpose(Xs[-1], [0, 2, 1]), fluid.layers.transpose(Xs[-1], [0, 2, 1]),
[-1, sparse_feature_dim, 1, [-1, self.sparse_feature_dim, 1,
last_s]) # None, embedding_size, 1, last_s last_s]) # None, embedding_size, 1, last_s
Z_k_1 = fluid.layers.matmul( Z_k_1 = fluid.layers.matmul(
X_0, X_k) # None, embedding_size, num_field, last_s X_0, X_k) # None, embedding_size, num_field, last_s
# compresses Z^(k+1) to X^(k+1) # compresses Z^(k+1) to X^(k+1)
Z_k_1 = fluid.layers.reshape(Z_k_1, [ Z_k_1 = fluid.layers.reshape(Z_k_1, [
-1, sparse_feature_dim, last_s * num_field -1, self.sparse_feature_dim, last_s * self.num_field
]) # None, embedding_size, last_s*num_field ]) # None, embedding_size, last_s*num_field
Z_k_1 = fluid.layers.transpose( Z_k_1 = fluid.layers.transpose(
Z_k_1, [0, 2, 1]) # None, s*num_field, embedding_size Z_k_1, [0, 2, 1]) # None, s*num_field, embedding_size
Z_k_1 = fluid.layers.reshape( Z_k_1 = fluid.layers.reshape(
Z_k_1, [-1, last_s * num_field, 1, sparse_feature_dim] Z_k_1,
[-1, last_s * self.num_field, 1, self.sparse_feature_dim]
) # None, last_s*num_field, 1, embedding_size (None, channal_in, h, w) ) # None, last_s*num_field, 1, embedding_size (None, channal_in, h, w)
X_k_1 = fluid.layers.conv2d( X_k_1 = fluid.layers.conv2d(
Z_k_1, Z_k_1,
...@@ -112,7 +118,8 @@ class Model(ModelBase): ...@@ -112,7 +118,8 @@ class Model(ModelBase):
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=initer)) # None, s, 1, embedding_size initializer=initer)) # None, s, 1, embedding_size
X_k_1 = fluid.layers.reshape( X_k_1 = fluid.layers.reshape(
X_k_1, [-1, s, sparse_feature_dim]) # None, s, embedding_size X_k_1,
[-1, s, self.sparse_feature_dim]) # None, s, embedding_size
Xs.append(X_k_1) Xs.append(X_k_1)
last_s = s last_s = s
...@@ -130,17 +137,13 @@ class Model(ModelBase): ...@@ -130,17 +137,13 @@ class Model(ModelBase):
# -------------------- DNN -------------------- # -------------------- DNN --------------------
layer_sizes_dnn = envs.get_global_env( y_dnn = fluid.layers.reshape(
"hyper_parameters.layer_sizes_dnn", None, self._namespace) feat_embeddings, [-1, self.num_field * self.sparse_feature_dim])
act = envs.get_global_env("hyper_parameters.act", None, for s in self.layer_sizes_dnn:
self._namespace)
y_dnn = fluid.layers.reshape(feat_embeddings,
[-1, num_field * sparse_feature_dim])
for s in layer_sizes_dnn:
y_dnn = fluid.layers.fc( y_dnn = fluid.layers.fc(
input=y_dnn, input=y_dnn,
size=s, size=s,
act=act, act=self.act,
param_attr=fluid.ParamAttr(initializer=initer), param_attr=fluid.ParamAttr(initializer=initer),
bias_attr=None) bias_attr=None)
y_dnn = fluid.layers.fc(input=y_dnn, y_dnn = fluid.layers.fc(input=y_dnn,
...@@ -152,11 +155,6 @@ class Model(ModelBase): ...@@ -152,11 +155,6 @@ class Model(ModelBase):
# ------------------- xDeepFM ------------------ # ------------------- xDeepFM ------------------
self.predict = fluid.layers.sigmoid(y_linear + y_cin + y_dnn) self.predict = fluid.layers.sigmoid(y_linear + y_cin + y_dnn)
def train_net(self):
self._init_slots()
self.xdeepfm_net()
cost = fluid.layers.log_loss( cost = fluid.layers.log_loss(
input=self.predict, input=self.predict,
label=fluid.layers.cast(self.label, "float32"), label=fluid.layers.cast(self.label, "float32"),
...@@ -172,12 +170,5 @@ class Model(ModelBase): ...@@ -172,12 +170,5 @@ class Model(ModelBase):
slide_steps=0) slide_steps=0)
self._metrics["AUC"] = auc_var self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
def optimizer(self): self._infer_results["AUC"] = auc_var
learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
None, self._namespace)
optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
return optimizer
def infer_net(self):
self.train_net()
models/recall/gru4rec/config.yaml
浏览文件 @ 88176619
...@@ -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 @ 88176619
...@@ -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 @ 60d1b7fc
# 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 @ 88176619
...@@ -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 @ 88176619
...@@ -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 @ 88176619
...@@ -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 @ 88176619
...@@ -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 @ 88176619
...@@ -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
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...@@ -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
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...@@ -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
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...@@ -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
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...@@ -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
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...@@ -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 @ 88176619
...@@ -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
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...@@ -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 | -- | -- |
setup.py
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...@@ -62,7 +62,8 @@ def build(dirname): ...@@ -62,7 +62,8 @@ def build(dirname):
models_copy = [ models_copy = [
'data/*.txt', 'data/*/*.txt', '*.yaml', '*.sh', 'tree/*.npy', 'data/*.txt', 'data/*/*.txt', '*.yaml', '*.sh', 'tree/*.npy',
'tree/*.txt', 'data/sample_data/*', 'data/sample_data/train/*', 'data/*/*.csv' 'tree/*.txt', 'data/sample_data/*', 'data/sample_data/train/*',
'data/sample_data/infer/*', 'data/*/*.csv'
] ]
engine_copy = ['*/*.sh'] engine_copy = ['*/*.sh']
......
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