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PaddleRec
“e6650ce89eed5a9251500b3fbb57f2910ed2f93b”上不存在“mobile/src/operators/kernel/cl/bilinear_interp_kernel.cpp”
未验证 提交 9a12e113 编写于 作者: T tangwei12 提交者： GitHub
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doc/pre_train_model.md
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......@@ -7,9 +7,27 @@ PaddleRec基于业务实践，使用真实数据，产出了推荐领域算法
### 获取地址
```bash
wget xxx.tar.gz
wget https://paddlerec.bj.bcebos.com/textcnn_pretrain%2Fpretrain_model.tar.gz
```
### 使用方法
解压后，得到的是一个paddle的模型文件夹，使用`PaddleRec/models/contentunderstanding/classification_finetue`模型进行加载
解压后，得到的是一个paddle的模型文件夹，使用`PaddleRec/models/contentunderstanding/textcnn`模型进行加载
您可以在PaddleRec/models/contentunderstanding/textcnn_pretrain中找到finetune_startup.py文件，在config.yaml中配置startup_class_path和init_pretraining_model_path两个参数。
在参数startup_class_path中配置finetune_startup.py文件的地址，在init_pretraining_model_path参数中配置您要加载的参数文件。
以textcnn_pretrain为例，配置完的runner如下：
```
runner:
- name: train_runner
class: train
epochs: 6
device: cpu
save_checkpoint_interval: 1
save_checkpoint_path: "increment"
init_model_path: ""
print_interval: 10
startup_class_path: "{workspace}/finetune_startup.py"
init_pretraining_model_path: "{workspace}/pretrain_model/pretrain_model_params"
phases: phase_train
```
具体使用方法请参照textcnn[使用预训练模型进行finetune](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/contentunderstanding/textcnn_pretrain)
doc/yaml.md
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......@@ -37,6 +37,8 @@
| startup_class_path | string | 路径 | 否 | 自定义startup流程实现的地址 |
| runner_class_path | string | 路径 | 否 | 自定义runner流程实现的地址 |
| terminal_class_path | string | 路径 | 否 | 自定义terminal流程实现的地址 |
| init_pretraining_model_path | string | 路径 | 否 |自定义的startup流程中需要传入这个参数，finetune中需要加载的参数的地址 |
......
models/contentunderstanding/readme.md
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# 内容理解模型库
## 简介
我们提供了常见的内容理解任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的内容理解模型包括 [Tagspace](tagspace)、[文本分类](classification)等。
我们提供了常见的内容理解任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的内容理解模型包括 [Tagspace](tagspace)、[文本分类](textcnn)、[基于textcnn的预训练模型](textcnn_pretrain)等。
模型算法库在持续添加中，欢迎关注。
......@@ -23,7 +23,7 @@
| 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: |
| TagSpace | 标签推荐 | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf) |
| Classification | 文本分类 | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
| textcnn | 文本分类 | [EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf) |
下面是每个模型的简介（注：图片引用自链接中的论文）
......@@ -32,7 +32,7 @@
<img align="center" src="../../doc/imgs/tagspace.png">
<p>
[文本分类CNN模型](https://www.aclweb.org/anthology/D14-1181.pdf)
[textCNN模型](https://www.aclweb.org/anthology/D14-1181.pdf)
<p align="center">
<img align="center" src="../../doc/imgs/cnn-ckim2014.png">
<p>
......@@ -42,7 +42,7 @@
git clone https://github.com/PaddlePaddle/PaddleRec.git paddle-rec
cd PaddleRec
python -m paddlerec.run -m models/contentunderstanding/tagspace/config.yaml
python -m paddlerec.run -m models/contentunderstanding/classification/config.yaml
python -m paddlerec.run -m models/contentunderstanding/textcnn/config.yaml
```
## 使用教程（复现论文）
......@@ -134,7 +134,7 @@ batch: 13, acc: [0.928], loss: [0.01736144]
batch: 14, acc: [0.93], loss: [0.01911209]
```
**（2）Classification**
**（2）textcnn**
### 数据处理
情感倾向分析（Sentiment Classification，简称Senta）针对带有主观描述的中文文本，可自动判断该文本的情感极性类别并给出相应的置信度。情感类型分为积极、消极。情感倾向分析能够帮助企业理解用户消费习惯、分析热点话题和危机舆情监控，为企业提供有利的决策支持。
......@@ -206,4 +206,4 @@ batch: 3, acc: [0.90234375], loss: [0.27907994]
| 数据集 | 模型 | loss | acc |
| :------------------: | :--------------------: | :---------: |:---------: |
| ag news dataset | TagSpace | 0.0198 | 0.9177 |
| ChnSentiCorp | Classification | 0.2282 | 0.9127 |
| ChnSentiCorp | textcnn | 0.2282 | 0.9127 |
models/contentunderstanding/classification/config.yaml → models/contentunderstanding/textcnn/config.yaml
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......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
workspace: "models/contentunderstanding/classification"
workspace: "models/contentunderstanding/textcnn"
dataset:
- name: data1
......
models/contentunderstanding/classification/readme.md → models/contentunderstanding/textcnn/readme.md
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# classification文本分类模型
# textcnn文本分类模型
以下是本例的简要目录结构及说明：
......@@ -15,7 +15,6 @@
├── config.yaml #配置文件
├── reader.py #读取程序
```
注：在阅读该示例前，建议您先了解以下内容：
[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md)
......@@ -73,13 +72,13 @@ os : windows/linux/macos
本文提供了样例数据可以供您快速体验，在paddlerec目录下直接执行下面的命令即可启动训练：
```
python -m paddlerec.run -m models/contentunderstanding/classification/config.yaml
python -m paddlerec.run -m models/contentunderstanding/textcnn/config.yaml
```
## 效果复现
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。如果需要复现readme中的效果,请按如下步骤依次操作即可。
1. 确认您当前所在目录为PaddleRec/models/contentunderstanding/classification
1. 确认您当前所在目录为PaddleRec/models/contentunderstanding/textcnn
2. 下载并解压数据集，命令如下：
```
wget https://baidu-nlp.bj.bcebos.com/sentiment_classification-dataset-1.0.0.tar.gz
......
models/contentunderstanding/textcnn_pretrain/__init__.py 0 → 100644
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# 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.
models/contentunderstanding/textcnn_pretrain/basemodel.py 0 → 100644
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# 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.
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
from paddlerec.core.metrics import RecallK
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.dict_size = 2000000 + 1
self.max_seq_len = 1024
self.emb_dim = 128
self.cnn_hid_dim = 128
self.cnn_win_size = 3
self.cnn_win_size2 = 5
self.hid_dim1 = 96
self.class_dim = 30
self.is_sparse = True
def input_data(self, is_infer=False, **kwargs):
text = fluid.data(
name="text", shape=[None, self.max_seq_len, 1], dtype='int64')
label = fluid.data(name="category", shape=[None, 1], dtype='int64')
seq_len = fluid.data(name="seq_len", shape=[None], dtype='int64')
return [text, label, seq_len]
def net(self, inputs, is_infer=False):
""" network definition """
#text label
self.data = inputs[0]
self.label = inputs[1]
self.seq_len = inputs[2]
emb = embedding(self.data, self.dict_size, self.emb_dim,
self.is_sparse)
concat = multi_convs(emb, self.seq_len, self.cnn_hid_dim,
self.cnn_win_size, self.cnn_win_size2)
self.fc_1 = full_connect(concat, self.hid_dim1)
self.metrics(is_infer)
def metrics(self, is_infer=False):
""" classification and metrics """
# softmax layer
prediction = fluid.layers.fc(input=[self.fc_1],
size=self.class_dim,
act="softmax",
name="pretrain_fc_1")
cost = fluid.layers.cross_entropy(input=prediction, label=self.label)
avg_cost = fluid.layers.mean(x=cost)
acc = fluid.layers.accuracy(input=prediction, label=self.label)
#acc = RecallK(input=prediction, label=label, k=1)
self._cost = avg_cost
if is_infer:
self._infer_results["acc"] = acc
else:
self._metrics["acc"] = acc
def embedding(inputs, dict_size, emb_dim, is_sparse):
""" embeding definition """
emb = fluid.layers.embedding(
input=inputs,
size=[dict_size, emb_dim],
is_sparse=is_sparse,
param_attr=fluid.ParamAttr(
name='pretrain_word_embedding',
initializer=fluid.initializer.Xavier()))
return emb
def multi_convs(input_layer, seq_len, cnn_hid_dim, cnn_win_size,
cnn_win_size2):
"""conv and concat"""
emb = fluid.layers.sequence_unpad(
input_layer, length=seq_len, name="pretrain_unpad")
conv = fluid.nets.sequence_conv_pool(
param_attr=fluid.ParamAttr(name="pretrain_conv0_w"),
bias_attr=fluid.ParamAttr(name="pretrain_conv0_b"),
input=emb,
num_filters=cnn_hid_dim,
filter_size=cnn_win_size,
act="tanh",
pool_type="max")
conv2 = fluid.nets.sequence_conv_pool(
param_attr=fluid.ParamAttr(name="pretrain_conv1_w"),
bias_attr=fluid.ParamAttr(name="pretrain_conv1_b"),
input=emb,
num_filters=cnn_hid_dim,
filter_size=cnn_win_size2,
act="tanh",
pool_type="max")
concat = fluid.layers.concat(
input=[conv, conv2], axis=1, name="pretrain_concat")
return concat
def full_connect(input_layer, hid_dim1):
"""full connect layer"""
fc_1 = fluid.layers.fc(name="pretrain_fc_0",
input=input_layer,
size=hid_dim1,
act="tanh")
return fc_1
models/contentunderstanding/textcnn_pretrain/config.yaml 0 → 100644
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# 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.
workspace: "models/contentunderstanding/textcnn_pretrain"
dataset:
- name: dataset_train
batch_size: 128
type: DataLoader
data_path: "{workspace}/senta_data/train"
data_converter: "{workspace}/reader.py"
- name: dataset_infer
batch_size: 256
type: DataLoader
data_path: "{workspace}/senta_data/test"
data_converter: "{workspace}/reader.py"
hyper_parameters:
optimizer:
class: adam
learning_rate: 0.001
strategy: async
mode: [train_runner,infer_runner]
runner:
- name: train_runner
class: train
epochs: 6
device: cpu
save_checkpoint_interval: 1
save_checkpoint_path: "increment"
init_model_path: ""
print_interval: 10
# startup class for finetuning
startup_class_path: "{workspace}/finetune_startup.py"
# path of pretrained model. Please set empty if you don't use finetune function.
init_pretraining_model_path: "{workspace}/pretrain_model/pretrain_model_params"
phases: phase_train
- name: infer_runner
class: infer
# device to run training or infer
device: cpu
print_interval: 1
init_model_path: "increment/3" # load model path
phases: phase_infer
phase:
- name: phase_train
model: "{workspace}/model.py"
dataset_name: dataset_train
thread_num: 1
- name: phase_infer
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_infer # select dataset by name
thread_num: 1
models/contentunderstanding/textcnn_pretrain/data/preprocess.py 0 → 100644
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# encoding=utf-8
import os
import sys
def build_word_dict():
word_file = "word_dict.txt"
f = open(word_file, "r")
word_dict = {}
lines = f.readlines()
for line in lines:
word = line.strip().split("\t")
word_dict[word[0]] = word[1]
f.close()
return word_dict
def build_token_data(word_dict, txt_file, token_file):
max_text_size = 100
f = open(txt_file, "r")
fout = open(token_file, "w")
lines = f.readlines()
i = 0
for line in lines:
line = line.strip("\n").split("\t")
text = line[0].strip("\n").split(" ")
tokens = []
label = line[1]
for word in text:
if word in word_dict:
tokens.append(str(word_dict[word]))
else:
tokens.append("0")
seg_len = len(tokens)
if seg_len < 5:
continue
if seg_len >= max_text_size:
tokens = tokens[:max_text_size]
seg_len = max_text_size
else:
tokens = tokens + ["0"] * (max_text_size - seg_len)
text_tokens = " ".join(tokens)
fout.write(text_tokens + " " + str(seg_len) + " " + label + "\n")
if (i + 1) % 100 == 0:
print(str(i + 1) + " lines OK")
i += 1
fout.close()
f.close()
word_dict = build_word_dict()
txt_file = "test.tsv"
token_file = "test.txt"
build_token_data(word_dict, txt_file, token_file)
txt_file = "dev.tsv"
token_file = "dev.txt"
build_token_data(word_dict, txt_file, token_file)
txt_file = "train.tsv"
token_file = "train.txt"
build_token_data(word_dict, txt_file, token_file)
models/contentunderstanding/textcnn_pretrain/data/test/test.txt 0 → 100644
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models/contentunderstanding/textcnn_pretrain/data/train/train.txt 0 → 100644
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2276 6737 24366 3574 9749 19038 30130 16576 3567 1118 13687 6988 2276 29434 32608 3574 6611 12211 13687 18448 6611 12211 9715 3574 10362 27285 23576 22559 2018 11474 2018 5314 2018 10707 11924 2019 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36 1
21470 18448 19675 12211 15567 3574 10074 2929 130 20245 19058 16576 2019 23445 2436 23954 3574 16687 5391 3574 20403 30604 2019 32557 19640 13757 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 1
10710 30781 18835 19433 3574 8697 9218 16576 19640 5354 6327 12211 29349 29128 3574 3406 16576 19814 12091 2019 17294 7604 12211 4002 18835 26370 14276 2019 19861 20255 25357 16576 8035 3334 3574 25469 8630 24073 113 4352 16523 12211 21264 21017 32365 16603 12211 2019 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 1
4852 23653 3574 14451 14132 15492 3574 4002 17937 32952 11231 28952 8053 16576 2019 31713 16576 29112 16191 3434 16576 15871 3574 17942 11533 19358 3574 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 1
models/contentunderstanding/textcnn_pretrain/finetune_startup.py 0 → 100644
浏览文件 @ 9a12e113
# 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
import warnings
import os
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddlerec.core.utils import envs
from paddlerec.core.trainers.framework.startup import StartupBase
from paddlerec.core.trainer import EngineMode
__all__ = ["Startup"]
class Startup(StartupBase):
"""R
"""
def __init__(self, context):
self.op_name_scope = "op_namescope"
self.clip_op_name_scope = "@CLIP"
self.op_role_var_attr_name = core.op_proto_and_checker_maker.kOpRoleVarAttrName(
)
print("Running FineTuningStartup.")
def _is_opt_role_op(self, op):
# NOTE: depend on oprole to find out whether this op is for
# optimize
op_maker = core.op_proto_and_checker_maker
optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize
if op_maker.kOpRoleAttrName() in op.attr_names and \
int(op.all_attrs()[op_maker.kOpRoleAttrName()]) == int(optimize_role):
return True
return False
def _get_params_grads(self, program):
"""
Get optimizer operators, parameters and gradients from origin_program
Returns:
opt_ops (list): optimize operators.
params_grads (dict): parameter->gradient.
"""
block = program.global_block()
params_grads = []
# tmp set to dedup
optimize_params = set()
origin_var_dict = program.global_block().vars
for op in block.ops:
if self._is_opt_role_op(op):
# Todo(chengmo): Whether clip related op belongs to Optimize guard should be discussed
# delete clip op from opt_ops when run in Parameter Server mode
if self.op_name_scope in op.all_attrs(
) and self.clip_op_name_scope in op.attr(self.op_name_scope):
op._set_attr(
"op_role",
int(core.op_proto_and_checker_maker.OpRole.Backward))
continue
if op.attr(self.op_role_var_attr_name):
param_name = op.attr(self.op_role_var_attr_name)[0]
grad_name = op.attr(self.op_role_var_attr_name)[1]
if not param_name in optimize_params:
optimize_params.add(param_name)
params_grads.append([
origin_var_dict[param_name],
origin_var_dict[grad_name]
])
return params_grads
@staticmethod
def is_persistable(var):
"""
Check whether the given variable is persistable.
Args:
var(Variable): The variable to be checked.
Returns:
bool: True if the given `var` is persistable
False if not.
Examples:
.. code-block:: python
import paddle.fluid as fluid
param = fluid.default_main_program().global_block().var('fc.b')
res = fluid.io.is_persistable(param)
"""
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.READER:
return False
return var.persistable
def load(self, context, is_fleet=False, main_program=None):
dirname = envs.get_global_env("runner." + context["runner_name"] +
".init_pretraining_model_path", "")
hotstart_dirname = envs.get_global_env(
"runner." + context["runner_name"] + ".init_model_path", "")
def existed_params(var):
if not isinstance(var, fluid.framework.Parameter):
return False
if os.path.exists(os.path.join(dirname, var.name)):
print("INIT %s" % var.name)
return True
else:
#print("SKIP %s" % var.name)
return False
if hotstart_dirname != "":
#If init_model_path exists, hot start is first choice
print("going to load ", hotstart_dirname)
fluid.io.load_persistables(
context["exe"], hotstart_dirname, main_program=main_program)
print("load from {} success".format(hotstart_dirname))
elif dirname != "":
#If init_pretraining_model_path exists ,pretrained model load parameters
print("going to load ", dirname)
fluid.io.load_vars(
context["exe"],
dirname,
main_program=main_program,
predicate=existed_params)
print("load from {} success".format(dirname))
else:
#If both of the above are empty, cold start model
return
def startup(self, context):
for model_dict in context["phases"]:
with fluid.scope_guard(context["model"][model_dict["name"]][
"scope"]):
train_prog = context["model"][model_dict["name"]][
"main_program"]
startup_prog = context["model"][model_dict["name"]][
"startup_program"]
with fluid.program_guard(train_prog, startup_prog):
context["exe"].run(startup_prog)
self.load(context, main_program=train_prog)
context["status"] = "train_pass"
models/contentunderstanding/textcnn_pretrain/model.py 0 → 100644
浏览文件 @ 9a12e113
# 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.
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
from basemodel import embedding
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.dict_size = 2000001
self.max_len = 100
self.cnn_dim = 128
self.cnn_filter_size1 = 1
self.cnn_filter_size2 = 2
self.cnn_filter_size3 = 3
self.emb_dim = 128
self.hid_dim = 96
self.class_dim = 2
self.is_sparse = True
def input_data(self, is_infer=False, **kwargs):
data = fluid.data(
name="input", shape=[None, self.max_len, 1], dtype='int64')
seq_len = fluid.data(name="seq_len", shape=[None], dtype='int64')
label = fluid.data(name="label", shape=[None, 1], dtype='int64')
return [data, seq_len, label]
def net(self, input, is_infer=False):
""" network definition """
self.data = input[0]
self.seq_len = input[1]
self.label = input[2]
# embedding layer
emb = embedding(self.data, self.dict_size, self.emb_dim,
self.is_sparse)
emb = fluid.layers.sequence_unpad(emb, length=self.seq_len)
# convolution layer
conv1 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=self.cnn_dim,
filter_size=self.cnn_filter_size1,
act="tanh",
pool_type="max")
conv2 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=self.cnn_dim,
filter_size=self.cnn_filter_size2,
act="tanh",
pool_type="max")
conv3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=self.cnn_dim,
filter_size=self.cnn_filter_size3,
act="tanh",
pool_type="max")
convs_out = fluid.layers.concat(input=[conv1, conv2, conv3], axis=1)
# full connect layer
fc_1 = fluid.layers.fc(input=convs_out, size=self.hid_dim, act="tanh")
# softmax layer
prediction = fluid.layers.fc(input=[fc_1],
size=self.class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=self.label)
avg_cost = fluid.layers.mean(x=cost)
acc = fluid.layers.accuracy(input=prediction, label=self.label)
self._cost = avg_cost
if is_infer:
self._infer_results["acc"] = acc
self._infer_results["loss"] = avg_cost
else:
self._metrics["acc"] = acc
self._metrics["loss"] = avg_cost
models/contentunderstanding/textcnn_pretrain/reader.py 0 → 100644
浏览文件 @ 9a12e113
# 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.
import sys
from paddlerec.core.reader import ReaderBase
class Reader(ReaderBase):
def init(self):
pass
def _process_line(self, l):
l = l.strip().split()
data = l[0:100]
seq_len = l[100:101]
label = l[101:]
return data, label, seq_len
def generate_sample(self, line):
def data_iter():
data, label, seq_len = self._process_line(line)
if data is None:
yield None
return
data = [int(i) for i in data]
label = [int(i) for i in label]
seq_len = [int(i) for i in seq_len]
yield [('data', data), ('seq_len', seq_len), ('label', label)]
return data_iter
models/contentunderstanding/textcnn_pretrain/readme.md 0 → 100644
浏览文件 @ 9a12e113
# 使用文本分类模型作为预训练模型对textcnn模型进行fine-tuning
以下是本例的简要目录结构及说明：
```
├── data #样例数据
├── train
├── train.txt #训练数据样例
├── test
├── test.txt #测试数据样例
├── preprocess.py #数据处理程序
├── __init__.py
├── README.md #文档
├── model.py #模型文件
├── basemodel.py #预训练模型
├── config.yaml #配置文件
├── reader.py #读取程序
├── finetune_startup.py #加载参数
```
注：在阅读该示例前，建议您先了解以下内容：
[paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md)
## 内容
- [模型简介](#模型简介)
- [数据准备](#数据准备)
- [运行环境](#运行环境)
- [快速开始](#快速开始)
- [效果复现](#效果复现)
- [进阶使用](#进阶使用)
- [FAQ](#FAQ)
## 模型简介
情感倾向分析（Sentiment Classification，简称Senta）针对带有主观描述的中文文本，可自动判断该文本的情感极性类别并给出相应的置信度。情感类型分为积极、消极。在本文中，我们提供了一个使用大规模的对文章数据进行多分类的textCNN模型（2个卷积核的cnn模型）作为预训练模型。本文会使用这个预训练模型对contentunderstanding目录下的textcnn模型（3个卷积核的cnn模型）进行fine-tuning。本文将预训练模型中的embedding层迁移到了contentunderstanding目录下的textcnn模型中，依然进行情感分析的二分类任务。最终获得了模型准确率上的基本持平以及更快速的收敛
Yoon Kim在论文[EMNLP 2014][Convolutional neural networks for sentence classication](https://www.aclweb.org/anthology/D14-1181.pdf)提出了TextCNN并给出基本的结构。将卷积神经网络CNN应用到文本分类任务，利用多个不同size的kernel来提取句子中的关键信息（类似于多窗口大小的ngram），从而能够更好地捕捉局部相关性。模型的主体结构如图所示：
<p align="center">
<img align="center" src="../../../doc/imgs/cnn-ckim2014.png">
<p>
## 数据准备
情感倾向分析（Sentiment Classification，简称Senta）针对带有主观描述的中文文本，可自动判断该文本的情感极性类别并给出相应的置信度。情感类型分为积极、消极。情感倾向分析能够帮助企业理解用户消费习惯、分析热点话题和危机舆情监控，为企业提供有利的决策支持。
情感是人类的一种高级智能行为，为了识别文本的情感倾向，需要深入的语义建模。另外，不同领域（如餐饮、体育）在情感的表达各不相同，因而需要有大规模覆盖各个领域的数据进行模型训练。为此，我们通过基于深度学习的语义模型和大规模数据挖掘解决上述两个问题。效果上，我们和contentunderstanding目录下的textcnn模型一样基于开源情感倾向分类数据集ChnSentiCorp进行评测。
您可以直接执行以下命令获取我们的预训练模型（basemodel.py，pretrain_model_params）以及对应的字典（word_dict.txt）：
```
wget https://paddlerec.bj.bcebos.com/textcnn_pretrain%2Fpretrain_model.tar.gz
tar -zxvf textcnn_pretrain%2Fpretrain_model.tar.gz
```
您可以直接执行以下命令下载我们分词完毕后的数据集,文件解压之后，senta_data目录下会存在训练数据（train.tsv）、开发集数据（dev.tsv）、测试集数据（test.tsv）以及对应的词典（word_dict.txt）：
```
wget https://baidu-nlp.bj.bcebos.com/sentiment_classification-dataset-1.0.0.tar.gz
tar -zxvf sentiment_classification-dataset-1.0.0.tar.gz
```
数据格式为一句中文的评价语句，和一个代表情感信息的标签。两者之间用/t分隔，中文的评价语句已经分词，词之间用空格分隔。
```
15.4寸 笔记本 的 键盘 确实 爽 ， 基本 跟 台式机 差不多 了 ， 蛮 喜欢 数字 小 键盘 ， 输 数字 特 方便 ， 样子 也 很 美观 ， 做工 也 相当 不错 1
跟 心灵 鸡汤 没 什么 本质 区别 嘛 ， 至少 我 不 喜欢 这样 读 经典 ， 把 经典 都 解读 成 这样 有点 去 中国 化 的 味道 了 0
```
## 运行环境
PaddlePaddle>=1.7.2
python 2.7/3.5/3.6/3.7
PaddleRec >=0.1
os : windows/linux/macos
## 快速开始
本文需要下载模型的参数文件和finetune的数据集才可以体现出finetune的效果，所以暂不提供快速一键运行。若想体验finetune的效果，请按照下面【效果复现】模块的步骤依次执行。
## 效果复现
在本模块，我们希望用户可以理解如何使用预训练模型来对自己的模型进行fine-tuning。
1. 确认您当前所在目录为PaddleRec/models/contentunderstanding/textcnn_pretrain
2. 下载并解压数据集，命令如下。解压后您可以看到出现senta_data目录
```
wget https://baidu-nlp.bj.bcebos.com/sentiment_classification-dataset-1.0.0.tar.gz
tar -zxvf sentiment_classification-dataset-1.0.0.tar.gz
```
3. 下载并解压预训练模型，命令如下。
```
wget https://paddlerec.bj.bcebos.com/textcnn_pretrain%2Fpretrain_model.tar.gz
tar -zxvf textcnn_pretrain%2Fpretrain_model.tar.gz
```
4. 本文提供了快速将数据集中的汉字数据处理为可训练格式数据的脚本。在您下载预训练模型后，将word_dict.txt复制到senta_data文件中。您在解压数据集后，将preprocess.py复制到senta_data文件中。
执行preprocess.py，即可将数据集中提供的dev.tsv，test.tsv，train.tsv按照词典提供的对应关系转化为可直接训练的txt文件.命令如下：
```
rm -f senta_data/word_dict.txt
cp pretrain_model/word_dict.txt senta_data
cp data/preprocess.py senta_data/
cd senta_data
python3 preprocess.py
mkdir train
mv train.txt train
mkdir test
mv test.txt test
cd ..
```
5. 打开文件config.yaml,更改其中的参数
将workspace改为您当前的绝对路径。（可用pwd命令获取绝对路径）
6. 执行命令，开始训练：
```
python -m paddlerec.run -m ./config.yaml
```
7. 运行结果：
```
PaddleRec: Runner infer_runner Begin
Executor Mode: infer
processor_register begin
Running SingleInstance.
Running SingleNetwork.
Running SingleInferStartup.
Running SingleInferRunner.
load persistables from increment/3
batch: 1, acc: [0.8828125], loss: [0.35940486]
batch: 2, acc: [0.91796875], loss: [0.24300358]
batch: 3, acc: [0.91015625], loss: [0.2490797]
Infer phase_infer of epoch increment/3 done, use time: 0.78388094902, global metrics: acc=[0.91015625], loss=[0.2490797]
PaddleRec Finish
```
## 进阶使用
在观察完model.py和config.yaml两个文件后，相信大家会发现和之前的模型相比有些改变。本章将详细解析这些改动，方便大家理解并灵活应用到自己的程序中.
1.在model.py中，大家会发现在构建embedding层的时候，直接传参使用了basemodel.py中的embeding层。
这是因为本文使用了预训练模型（basemodel.py）中embedding层，经过大量语料的训练后的embedding层中本身已经蕴含了大量的先验知识。而这些先验知识对于下游任务，尤其是小数据集来讲，是非常有帮助的。
2.在config.yaml中，大家会发现在train_runner中多了startup_class_path和init_pretraining_model_path两个参数。
参数startup_class_path的作用是自定义训练的流程。我们将在自定义的finetune_startup.py文件中将训练好的参数加载入模型当中。
参数init_pretraining_model_path的作用就是指明加载参数的路径。若路径下的参数文件和模型中的var具有相同的名字，就会将参数加载进模型当中。
在您设置init_model_path参数时，程序会优先试图按您设置的路径热启动。当没有init_model_path参数，无法热启动时，程序会试图加载init_pretraining_model_path路径下的参数，进行finetune训练。
只有在两者均为空的情况下，模型会冷启动从头开始训练。
若您希望进一步了解自定义流程的操作，可以参考以下内容：[如何添加自定义流程](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/trainer_develop.md#%E5%A6%82%E4%BD%95%E6%B7%BB%E5%8A%A0%E8%87%AA%E5%AE%9A%E4%B9%89%E6%B5%81%E7%A8%8B)
3.在basemodel.py中，我们准备了embedding，multi_convs，full_connect三个模块供您在有需要时直接import使用。
相关参数可以从本文提供的预训练模型下载链接里的pretrain_model/pretrain_model_params中找到。
## FAQ
tools/build_script.sh
浏览文件 @ 9a12e113
......@@ -49,7 +49,7 @@ function model_test() {
root_dir=`pwd`
all_model=$(find ${root_dir} -name config.yaml)
special_models=("demo" "pnn" "fgcnn" "gru4rec" "tagspace")
special_models=("demo" "pnn" "fgcnn" "gru4rec" "tagspace" "textcnn_pretrain")
for model in ${all_model}
do
......
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