diff --git a/README.md b/README.md index ed205a677bcb1136696a5bdc2e6dadca4f079094..84c53d2a06ee7b52ae7c89187fb0316730390f01 100644 --- a/README.md +++ b/README.md @@ -147,6 +147,7 @@ python -m paddlerec.run -m models/rank/dnn/config.yaml * [启动分布式训练](doc/distributed_train.md) * [启动预测](doc/predict.md) * [快速部署](doc/serving.md) +* [预训练模型](doc/pre_train_model.md) ### 进阶教程 diff --git a/core/engine/cluster/cloud/k8s_config.ini.template b/core/engine/cluster/cloud/k8s_config.ini.template index 8979cc6f0d996c132fbc2259a01134ba4a8a1ee5..471bd1a0dd2931591b0d6eda7f87cc25458b3f80 100644 --- a/core/engine/cluster/cloud/k8s_config.ini.template +++ b/core/engine/cluster/cloud/k8s_config.ini.template @@ -19,6 +19,7 @@ afs_local_mount_point="/root/paddlejob/workspace/env_run/afs/" # 新k8s afs挂载帮助文档: http://wiki.baidu.com/pages/viewpage.action?pageId=906443193 PADDLE_PADDLEREC_ROLE=WORKER +PADDLEREC_CLUSTER_TYPE=K8S use_python3=<$ USE_PYTHON3 $> CPU_NUM=<$ CPU_NUM $> GLOG_v=0 diff --git a/core/engine/cluster/cloud/mpi_config.ini.template b/core/engine/cluster/cloud/mpi_config.ini.template index 7d9f7fbb97a53c23e566e925a87eae990cef9f2a..a3ac22f0c7fc09e9b6eda44306972dd296d19ab7 100644 --- a/core/engine/cluster/cloud/mpi_config.ini.template +++ b/core/engine/cluster/cloud/mpi_config.ini.template @@ -17,6 +17,7 @@ output_path=<$ OUTPUT_PATH $> thirdparty_path=<$ THIRDPARTY_PATH $> PADDLE_PADDLEREC_ROLE=WORKER +PADDLEREC_CLUSTER_TYPE=MPI use_python3=<$ USE_PYTHON3 $> CPU_NUM=<$ CPU_NUM $> GLOG_v=0 diff --git a/core/factory.py b/core/factory.py index 9430c88283800e69db7043aa141b6f735212c79f..95e0e7778141ad76d1166205213bccdaae67aff7 100755 --- a/core/factory.py +++ b/core/factory.py @@ -22,6 +22,19 @@ trainers = {} def trainer_registry(): + trainers["SingleTrainer"] = os.path.join(trainer_abs, "single_trainer.py") + trainers["ClusterTrainer"] = os.path.join(trainer_abs, + "cluster_trainer.py") + trainers["CtrCodingTrainer"] = os.path.join(trainer_abs, + "ctr_coding_trainer.py") + trainers["CtrModulTrainer"] = os.path.join(trainer_abs, + "ctr_modul_trainer.py") + trainers["TDMSingleTrainer"] = os.path.join(trainer_abs, + "tdm_single_trainer.py") + trainers["TDMClusterTrainer"] = os.path.join(trainer_abs, + "tdm_cluster_trainer.py") + trainers["OnlineLearningTrainer"] = os.path.join( + trainer_abs, "online_learning_trainer.py") # Definition of procedure execution process trainers["CtrCodingTrainer"] = os.path.join(trainer_abs, "ctr_coding_trainer.py") diff --git a/core/trainer.py b/core/trainer.py index 8b1afd449a70265d5bcae9996d42795a1235197a..bbba6250529283d24389e2719b7110f8aa321973 100755 --- a/core/trainer.py +++ b/core/trainer.py @@ -107,6 +107,7 @@ class Trainer(object): self.device = Device.GPU gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0)) self._place = fluid.CUDAPlace(gpu_id) + print("PaddleRec run on device GPU: {}".format(gpu_id)) self._exe = fluid.Executor(self._place) elif device == "CPU": self.device = Device.CPU @@ -146,6 +147,7 @@ class Trainer(object): elif engine.upper() == "CLUSTER": self.engine = EngineMode.CLUSTER self.is_fleet = True + self.which_cluster_type() else: raise ValueError("Not Support Engine {}".format(engine)) self._context["is_fleet"] = self.is_fleet @@ -165,6 +167,14 @@ class Trainer(object): self._context["is_pslib"] = (fleet_mode.upper() == "PSLIB") self._context["fleet_mode"] = fleet_mode + def which_cluster_type(self): + cluster_type = os.getenv("PADDLEREC_CLUSTER_TYPE", "MPI") + print("PADDLEREC_CLUSTER_TYPE: {}".format(cluster_type)) + if cluster_type and cluster_type.upper() == "K8S": + self._context["cluster_type"] = "K8S" + else: + self._context["cluster_type"] = "MPI" + def which_executor_mode(self): executor_mode = envs.get_runtime_environ("train.trainer.executor_mode") if executor_mode.upper() not in ["TRAIN", "INFER"]: diff --git a/core/trainers/finetuning_trainer.py b/core/trainers/finetuning_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..4525a18867ff232121256c876c185c502427c130 --- /dev/null +++ b/core/trainers/finetuning_trainer.py @@ -0,0 +1,140 @@ +# 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. +""" +General Trainer, applicable to many situations: Single/Cluster/Local_Cluster + PS/COLLECTIVE +""" +from __future__ import print_function + +import os + +from paddlerec.core.utils import envs +from paddlerec.core.trainer import Trainer, EngineMode, FleetMode + + +class FineTuningTrainer(Trainer): + """ + Trainer for various situations + """ + + def __init__(self, config=None): + Trainer.__init__(self, config) + self.processor_register() + self.abs_dir = os.path.dirname(os.path.abspath(__file__)) + self.runner_env_name = "runner." + self._context["runner_name"] + + def processor_register(self): + print("processor_register begin") + self.regist_context_processor('uninit', self.instance) + self.regist_context_processor('network_pass', self.network) + self.regist_context_processor('startup_pass', self.startup) + self.regist_context_processor('train_pass', self.runner) + self.regist_context_processor('terminal_pass', self.terminal) + + def instance(self, context): + instance_class_path = envs.get_global_env( + self.runner_env_name + ".instance_class_path", default_value=None) + if instance_class_path: + instance_class = envs.lazy_instance_by_fliename( + instance_class_path, "Instance")(context) + else: + if self.engine == EngineMode.SINGLE: + instance_class_name = "SingleInstance" + else: + raise ValueError( + "FineTuningTrainer can only support SingleTraining.") + + instance_path = os.path.join(self.abs_dir, "framework", + "instance.py") + + instance_class = envs.lazy_instance_by_fliename( + instance_path, instance_class_name)(context) + + instance_class.instance(context) + + def network(self, context): + network_class_path = envs.get_global_env( + self.runner_env_name + ".network_class_path", default_value=None) + if network_class_path: + network_class = envs.lazy_instance_by_fliename(network_class_path, + "Network")(context) + else: + if self.engine == EngineMode.SINGLE: + network_class_name = "FineTuningNetwork" + else: + raise ValueError( + "FineTuningTrainer can only support SingleTraining.") + + network_path = os.path.join(self.abs_dir, "framework", + "network.py") + network_class = envs.lazy_instance_by_fliename( + network_path, network_class_name)(context) + + network_class.build_network(context) + + def startup(self, context): + startup_class_path = envs.get_global_env( + self.runner_env_name + ".startup_class_path", default_value=None) + if startup_class_path: + startup_class = envs.lazy_instance_by_fliename(startup_class_path, + "Startup")(context) + else: + if self.engine == EngineMode.SINGLE and not context["is_infer"]: + startup_class_name = "FineTuningStartup" + else: + raise ValueError( + "FineTuningTrainer can only support SingleTraining.") + + startup_path = os.path.join(self.abs_dir, "framework", + "startup.py") + + startup_class = envs.lazy_instance_by_fliename( + startup_path, startup_class_name)(context) + startup_class.startup(context) + + def runner(self, context): + runner_class_path = envs.get_global_env( + self.runner_env_name + ".runner_class_path", default_value=None) + if runner_class_path: + runner_class = envs.lazy_instance_by_fliename(runner_class_path, + "Runner")(context) + else: + if self.engine == EngineMode.SINGLE and not context["is_infer"]: + runner_class_name = "SingleRunner" + else: + raise ValueError( + "FineTuningTrainer can only support SingleTraining.") + + runner_path = os.path.join(self.abs_dir, "framework", "runner.py") + runner_class = envs.lazy_instance_by_fliename( + runner_path, runner_class_name)(context) + runner_class.run(context) + + def terminal(self, context): + terminal_class_path = envs.get_global_env( + self.runner_env_name + ".terminal_class_path", default_value=None) + if terminal_class_path: + terminal_class = envs.lazy_instance_by_fliename( + terminal_class_path, "Terminal")(context) + terminal_class.terminal(context) + else: + terminal_class_name = "TerminalBase" + if self.engine != EngineMode.SINGLE and self.fleet_mode != FleetMode.COLLECTIVE: + terminal_class_name = "PSTerminal" + + terminal_path = os.path.join(self.abs_dir, "framework", + "terminal.py") + terminal_class = envs.lazy_instance_by_fliename( + terminal_path, terminal_class_name)(context) + terminal_class.terminal(context) + context['is_exit'] = True diff --git a/core/trainers/framework/dataset.py b/core/trainers/framework/dataset.py index 8059eeb09a482671b8329fb88f5b52cfd64f163b..239b568be34793c5ddb0830e9cca06951da143f4 100644 --- a/core/trainers/framework/dataset.py +++ b/core/trainers/framework/dataset.py @@ -21,7 +21,7 @@ from paddlerec.core.utils import envs from paddlerec.core.utils import dataloader_instance from paddlerec.core.reader import SlotReader from paddlerec.core.trainer import EngineMode -from paddlerec.core.utils.util import split_files +from paddlerec.core.utils.util import split_files, check_filelist __all__ = ["DatasetBase", "DataLoader", "QueueDataset"] @@ -68,6 +68,8 @@ class DataLoader(DatasetBase): reader_ins = SlotReader(context["config_yaml"]) if hasattr(reader_ins, 'generate_batch_from_trainfiles'): dataloader.set_sample_list_generator(reader) + elif hasattr(reader_ins, 'batch_tensor_creator'): + dataloader.set_batch_generator(reader) else: dataloader.set_sample_generator(reader, batch_size) return dataloader @@ -119,14 +121,30 @@ class QueueDataset(DatasetBase): dataset.set_pipe_command(pipe_cmd) train_data_path = envs.get_global_env(name + "data_path") - file_list = [ - os.path.join(train_data_path, x) - for x in os.listdir(train_data_path) - ] + hidden_file_list, file_list = check_filelist( + hidden_file_list=[], + data_file_list=[], + train_data_path=train_data_path) + if (hidden_file_list is not None): + print( + "Warning:please make sure there are no hidden files in the dataset folder and check these hidden files:{}". + format(hidden_file_list)) + + file_list.sort() + need_split_files = False if context["engine"] == EngineMode.LOCAL_CLUSTER: + # for local cluster: split files for multi process + need_split_files = True + elif context["engine"] == EngineMode.CLUSTER and context[ + "cluster_type"] == "K8S": + # for k8s mount afs, split files for every node + need_split_files = True + + if need_split_files: file_list = split_files(file_list, context["fleet"].worker_index(), context["fleet"].worker_num()) print("File_list: {}".format(file_list)) + dataset.set_filelist(file_list) for model_dict in context["phases"]: if model_dict["dataset_name"] == dataset_name: diff --git a/core/trainers/framework/network.py b/core/trainers/framework/network.py index d2a6b71e4f74a639095eb404a82c9c1fefaf7fdf..7d7a8273b6a402bd163f653a7beb3900de899ae3 100644 --- a/core/trainers/framework/network.py +++ b/core/trainers/framework/network.py @@ -23,7 +23,7 @@ from paddlerec.core.trainers.framework.dataset import DataLoader, QueueDataset __all__ = [ "NetworkBase", "SingleNetwork", "PSNetwork", "PslibNetwork", - "CollectiveNetwork" + "CollectiveNetwork", "FineTuningNetwork" ] @@ -109,6 +109,88 @@ class SingleNetwork(NetworkBase): context["status"] = "startup_pass" +class FineTuningNetwork(NetworkBase): + """R + """ + + def __init__(self, context): + print("Running FineTuningNetwork.") + + def build_network(self, context): + context["model"] = {} + for model_dict in context["phases"]: + context["model"][model_dict["name"]] = {} + train_program = fluid.Program() + startup_program = fluid.Program() + scope = fluid.Scope() + dataset_name = model_dict["dataset_name"] + + with fluid.program_guard(train_program, startup_program): + with fluid.unique_name.guard(): + with fluid.scope_guard(scope): + model_path = envs.os_path_adapter( + envs.workspace_adapter(model_dict["model"])) + model = envs.lazy_instance_by_fliename( + model_path, "Model")(context["env"]) + + model._data_var = model.input_data( + dataset_name=model_dict["dataset_name"]) + + if envs.get_global_env("dataset." + dataset_name + + ".type") == "DataLoader": + model._init_dataloader( + is_infer=context["is_infer"]) + data_loader = DataLoader(context) + data_loader.get_dataloader(context, dataset_name, + model._data_loader) + + model.net(model._data_var, context["is_infer"]) + + finetuning_varnames = envs.get_global_env( + "runner." + context["runner_name"] + + ".finetuning_aspect_varnames", + default_value=[]) + + if len(finetuning_varnames) == 0: + raise ValueError( + "nothing need to be fine tuning, you may use other traning mode" + ) + + if len(finetuning_varnames) != 1: + raise ValueError( + "fine tuning mode can only accept one varname now" + ) + + varname = finetuning_varnames[0] + finetuning_vars = train_program.global_block().vars[ + varname] + finetuning_vars.stop_gradient = True + optimizer = model.optimizer() + optimizer.minimize(model._cost) + + context["model"][model_dict["name"]][ + "main_program"] = train_program + context["model"][model_dict["name"]][ + "startup_program"] = startup_program + context["model"][model_dict["name"]]["scope"] = scope + context["model"][model_dict["name"]]["model"] = model + context["model"][model_dict["name"]][ + "default_main_program"] = train_program.clone() + context["model"][model_dict["name"]]["compiled_program"] = None + + context["dataset"] = {} + for dataset in context["env"]["dataset"]: + type = envs.get_global_env("dataset." + dataset["name"] + ".type") + + if type == "QueueDataset": + dataset_class = QueueDataset(context) + context["dataset"][dataset[ + "name"]] = dataset_class.create_dataset(dataset["name"], + context) + + context["status"] = "startup_pass" + + class PSNetwork(NetworkBase): def __init__(self, context): print("Running PSNetwork.") diff --git a/core/trainers/framework/runner.py b/core/trainers/framework/runner.py index 761c6a748d9875f12ff48fb68da4822690fe8790..b7c67cd1d4f1b8702b1554e042114fb5e413b824 100644 --- a/core/trainers/framework/runner.py +++ b/core/trainers/framework/runner.py @@ -16,6 +16,7 @@ from __future__ import print_function import os import time +import warnings import numpy as np import logging import paddle.fluid as fluid @@ -301,6 +302,7 @@ class RunnerBase(object): return (epoch_id + 1) % epoch_interval == 0 def save_inference_model(): + # get global env name = "runner." + context["runner_name"] + "." save_interval = int( envs.get_global_env(name + "save_inference_interval", -1)) @@ -313,18 +315,44 @@ class RunnerBase(object): if feed_varnames is None or fetch_varnames is None or feed_varnames == "" or fetch_varnames == "" or \ len(feed_varnames) == 0 or len(fetch_varnames) == 0: return - fetch_vars = [ - fluid.default_main_program().global_block().vars[varname] - for varname in fetch_varnames - ] + + # check feed var exist + for var_name in feed_varnames: + if var_name not in fluid.default_main_program().global_block( + ).vars: + raise ValueError( + "Feed variable: {} not in default_main_program, global block has follow vars: {}". + format(var_name, + fluid.default_main_program().global_block() + .vars.keys())) + + # check fetch var exist + fetch_vars = [] + for var_name in fetch_varnames: + if var_name not in fluid.default_main_program().global_block( + ).vars: + raise ValueError( + "Fetch variable: {} not in default_main_program, global block has follow vars: {}". + format(var_name, + fluid.default_main_program().global_block() + .vars.keys())) + else: + fetch_vars.append(fluid.default_main_program() + .global_block().vars[var_name]) + dirname = envs.get_global_env(name + "save_inference_path", None) assert dirname is not None dirname = os.path.join(dirname, str(epoch_id)) if is_fleet: - context["fleet"].save_inference_model( - context["exe"], dirname, feed_varnames, fetch_vars) + warnings.warn( + "Save inference model in cluster training is not recommended! Using save checkpoint instead.", + category=UserWarning, + stacklevel=2) + if context["fleet"].worker_index() == 0: + context["fleet"].save_inference_model( + context["exe"], dirname, feed_varnames, fetch_vars) else: fluid.io.save_inference_model(dirname, feed_varnames, fetch_vars, context["exe"]) @@ -340,7 +368,8 @@ class RunnerBase(object): return dirname = os.path.join(dirname, str(epoch_id)) if is_fleet: - context["fleet"].save_persistables(context["exe"], dirname) + if context["fleet"].worker_index() == 0: + context["fleet"].save_persistables(context["exe"], dirname) else: fluid.io.save_persistables(context["exe"], dirname) diff --git a/core/trainers/framework/startup.py b/core/trainers/framework/startup.py index 362592e6de64a4bbfecb6868726b4a733edf4e14..a38dbd5bb3c2cea268fc5551e10e488f2fbdabd6 100644 --- a/core/trainers/framework/startup.py +++ b/core/trainers/framework/startup.py @@ -17,9 +17,13 @@ from __future__ import print_function import warnings import paddle.fluid as fluid +import paddle.fluid.core as core from paddlerec.core.utils import envs -__all__ = ["StartupBase", "SingleStartup", "PSStartup", "CollectiveStartup"] +__all__ = [ + "StartupBase", "SingleStartup", "PSStartup", "CollectiveStartup", + "FineTuningStartup" +] class StartupBase(object): @@ -65,6 +69,122 @@ class SingleStartup(StartupBase): context["status"] = "train_pass" +class FineTuningStartup(StartupBase): + """R + """ + + def __init__(self, context): + self.op_name_scope = "op_namescope" + self.clip_op_name_scope = "@CLIP" + self.self.op_role_var_attr_name = core.op_proto_and_checker_maker.kOpRoleVarAttrName( + ) + + print("Running SingleStartup.") + + 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_model_path", None) + if dirname is None or dirname == "": + return + print("going to load ", dirname) + + params_grads = self._get_params_grads(main_program) + update_params = [p for p, _ in params_grads] + need_load_vars = [] + parameters = list( + filter(FineTuningStartup.is_persistable, main_program.list_vars())) + + for param in parameters: + if param not in update_params: + need_load_vars.append(param) + + fluid.io.load_vars(context["exe"], dirname, main_program, + need_load_vars) + print("load from {} success".format(dirname)) + + 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" + + class PSStartup(StartupBase): def __init__(self, context): print("Running PSStartup.") diff --git a/core/utils/dataloader_instance.py b/core/utils/dataloader_instance.py index 2461473aa79a51133db8aa319f4ee7d45981d815..03e6f0a67884917e9af2d02d13eb86576620ceef 100755 --- a/core/utils/dataloader_instance.py +++ b/core/utils/dataloader_instance.py @@ -19,7 +19,7 @@ from paddlerec.core.utils.envs import get_global_env from paddlerec.core.utils.envs import get_runtime_environ from paddlerec.core.reader import SlotReader from paddlerec.core.trainer import EngineMode -from paddlerec.core.utils.util import split_files +from paddlerec.core.utils.util import split_files, check_filelist def dataloader_by_name(readerclass, @@ -38,11 +38,27 @@ def dataloader_by_name(readerclass, assert package_base is not None data_path = os.path.join(package_base, data_path.split("::")[1]) - files = [str(data_path) + "/%s" % x for x in os.listdir(data_path)] + hidden_file_list, files = check_filelist( + hidden_file_list=[], data_file_list=[], train_data_path=data_path) + if (hidden_file_list is not None): + print( + "Warning:please make sure there are no hidden files in the dataset folder and check these hidden files:{}". + format(hidden_file_list)) + + files.sort() + + need_split_files = False if context["engine"] == EngineMode.LOCAL_CLUSTER: + # for local cluster: split files for multi process + need_split_files = True + elif context["engine"] == EngineMode.CLUSTER and context[ + "cluster_type"] == "K8S": + # for k8s mount mode, split files for every node + need_split_files = True + print("need_split_files: {}".format(need_split_files)) + if need_split_files: files = split_files(files, context["fleet"].worker_index(), context["fleet"].worker_num()) - print("file_list : {}".format(files)) reader = reader_class(yaml_file) reader.init() @@ -67,6 +83,10 @@ def dataloader_by_name(readerclass, if hasattr(reader, 'generate_batch_from_trainfiles'): return gen_batch_reader() + + if hasattr(reader, "batch_tensor_creator"): + return reader.batch_tensor_creator(gen_reader) + return gen_reader @@ -80,11 +100,27 @@ def slotdataloader_by_name(readerclass, dataset_name, yaml_file, context): assert package_base is not None data_path = os.path.join(package_base, data_path.split("::")[1]) - files = [str(data_path) + "/%s" % x for x in os.listdir(data_path)] + hidden_file_list, files = check_filelist( + hidden_file_list=[], data_file_list=[], train_data_path=data_path) + if (hidden_file_list is not None): + print( + "Warning:please make sure there are no hidden files in the dataset folder and check these hidden files:{}". + format(hidden_file_list)) + + files.sort() + + need_split_files = False if context["engine"] == EngineMode.LOCAL_CLUSTER: + # for local cluster: split files for multi process + need_split_files = True + elif context["engine"] == EngineMode.CLUSTER and context[ + "cluster_type"] == "K8S": + # for k8s mount mode, split files for every node + need_split_files = True + + if need_split_files: files = split_files(files, context["fleet"].worker_index(), context["fleet"].worker_num()) - print("file_list: {}".format(files)) sparse = get_global_env(name + "sparse_slots", "#") if sparse == "": @@ -134,11 +170,27 @@ def slotdataloader(readerclass, train, yaml_file, context): assert package_base is not None data_path = os.path.join(package_base, data_path.split("::")[1]) - files = [str(data_path) + "/%s" % x for x in os.listdir(data_path)] + hidden_file_list, files = check_filelist( + hidden_file_list=[], data_file_list=[], train_data_path=data_path) + if (hidden_file_list is not None): + print( + "Warning:please make sure there are no hidden files in the dataset folder and check these hidden files:{}". + format(hidden_file_list)) + + files.sort() + + need_split_files = False if context["engine"] == EngineMode.LOCAL_CLUSTER: + # for local cluster: split files for multi process + need_split_files = True + elif context["engine"] == EngineMode.CLUSTER and context[ + "cluster_type"] == "K8S": + # for k8s mount mode, split files for every node + need_split_files = True + + if need_split_files: files = split_files(files, context["fleet"].worker_index(), context["fleet"].worker_num()) - print("file_list: {}".format(files)) sparse = get_global_env("sparse_slots", "#", namespace) if sparse == "": diff --git a/core/utils/util.py b/core/utils/util.py index 4eba912cafda6619ba37c3f8bc170d7d41ea40c4..f6acfe203612326a77f41326581583278dac4183 100755 --- a/core/utils/util.py +++ b/core/utils/util.py @@ -201,6 +201,28 @@ def split_files(files, trainer_id, trainers): return trainer_files[trainer_id] +def check_filelist(hidden_file_list, data_file_list, train_data_path): + for root, dirs, files in os.walk(train_data_path): + if (files == None and dirs == None): + return None, None + else: + # use files and dirs + for file_name in files: + file_path = os.path.join(train_data_path, file_name) + if file_name[0] == '.': + hidden_file_list.append(file_path) + else: + data_file_list.append(file_path) + for dirs_name in dirs: + dirs_path = os.path.join(train_data_path, dirs_name) + if dirs_name[0] == '.': + hidden_file_list.append(dirs_path) + else: + #train_data_path = os.path.join(train_data_path, dirs_name) + check_filelist(hidden_file_list, data_file_list, dirs_path) + return hidden_file_list, data_file_list + + class CostPrinter(object): """ For count cost time && print cost log diff --git a/doc/imgs/match-pyramid.png b/doc/imgs/match-pyramid.png new file mode 100644 index 0000000000000000000000000000000000000000..8692150752a669a8ece98f864e8c58b32b8c4a79 Binary files /dev/null and b/doc/imgs/match-pyramid.png differ diff --git a/doc/imgs/w2v_train.png b/doc/imgs/w2v_train.png new file mode 100644 index 0000000000000000000000000000000000000000..b32aa14327003e138ec7ccbf035866c4bf73edf7 Binary files /dev/null and b/doc/imgs/w2v_train.png differ diff --git a/doc/pre_train_model.md b/doc/pre_train_model.md new file mode 100644 index 0000000000000000000000000000000000000000..134710a430992cc756cd37fcc1e01ee3aef2dfb1 --- /dev/null +++ b/doc/pre_train_model.md @@ -0,0 +1,15 @@ +# PaddleRec 预训练模型 + +PaddleRec基于业务实践,使用真实数据,产出了推荐领域算法的若干预训练模型,方便开发者进行算法调研。 + +## 文本分类预训练模型 + +### 获取地址 + +```bash +wget xxx.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 diff --git a/models/contentunderstanding/classification/config.yaml b/models/contentunderstanding/classification/config.yaml index 0b76646d3ee2ecbcbb5a93d34726679e1761b4c6..70a439afbf1348621f64a810e61b4c504ad91012 100644 --- a/models/contentunderstanding/classification/config.yaml +++ b/models/contentunderstanding/classification/config.yaml @@ -16,9 +16,14 @@ workspace: "models/contentunderstanding/classification" dataset: - name: data1 - batch_size: 5 + batch_size: 10 type: DataLoader - data_path: "{workspace}/data/train_data" + data_path: "{workspace}/data/train" + data_converter: "{workspace}/reader.py" +- name: dataset_infer + batch_size: 2 + type: DataLoader + data_path: "{workspace}/data/test" data_converter: "{workspace}/reader.py" hyper_parameters: @@ -26,23 +31,47 @@ hyper_parameters: class: Adagrad learning_rate: 0.001 is_sparse: False + dict_dim: 33257 + max_len: 100 + cnn_dim: 128 + cnn_filter_size1: 1 + cnn_filter_size2: 2 + cnn_filter_size3: 3 + emb_dim: 128 + hid_dim: 96 + class_dim: 2 -mode: runner1 +mode: [train_runner,infer_runner] runner: -- name: runner1 +- name: train_runner class: train - epochs: 10 + epochs: 16 device: cpu - save_checkpoint_interval: 2 - save_inference_interval: 4 + save_checkpoint_interval: 1 + save_inference_interval: 1 save_checkpoint_path: "increment" save_inference_path: "inference" save_inference_feed_varnames: [] save_inference_fetch_varnames: [] + init_model_path: "" + print_interval: 10 + phases: phase_train +- name: infer_runner + class: infer + # device to run training or infer + device: cpu + print_interval: 1 + init_model_path: "increment/14" # load model path + phases: phase_infer + phase: -- name: phase1 +- name: phase_train model: "{workspace}/model.py" dataset_name: data1 thread_num: 1 +- name: phase_infer + model: "{workspace}/model.py" # user-defined model + dataset_name: dataset_infer # select dataset by name + thread_num: 1 diff --git a/models/contentunderstanding/classification/data/preprocess.py b/models/contentunderstanding/classification/data/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..4d59038c41d9b81c03822cad1b0d51b3627d74f3 --- /dev/null +++ b/models/contentunderstanding/classification/data/preprocess.py @@ -0,0 +1,65 @@ +# encoding=utf-8 +import os +import sys + + +def build_word_dict(): + word_file = "word_dict.txt" + f = open(word_file, "r") + lines = f.readlines() + word_list_ids = range(1, len(lines) + 1) + word_dict = dict(zip([word.strip() for word in lines], word_list_ids)) + 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) diff --git a/models/contentunderstanding/classification/data/test/test.txt b/models/contentunderstanding/classification/data/test/test.txt new file mode 100644 index 0000000000000000000000000000000000000000..d5dd69e3f35c29bb02f070be16a0af02ecfeae89 --- /dev/null +++ b/models/contentunderstanding/classification/data/test/test.txt @@ -0,0 +1,20 @@ +5681 17044 4352 7574 16576 3574 32952 12211 18835 28961 15320 2019 21675 30604 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 1 +9054 31881 4449 12211 12488 5975 3574 28592 2547 2547 14132 3574 24908 5975 24285 10010 3574 31872 20925 9886 12211 26530 3567 30818 19640 22506 28312 19887 12211 28212 8576 3574 28592 12306 14132 539 33049 9039 14160 113 3567 19675 5511 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14 48 29 29 45 5 0 -58 20 4 0 69 99 15 4 16 4 1 1 -93 30 90 5 23 63 25 30 99 32 7 1 -91 23 20 26 84 78 58 76 58 90 5 1 -33 2 36 59 55 9 79 34 92 57 9 0 -80 63 84 73 22 40 70 94 59 34 5 0 -49 95 50 32 90 22 18 66 46 32 2 0 -47 72 3 94 33 78 87 43 11 67 5 0 -76 44 86 81 95 48 79 46 11 65 8 1 -59 51 97 75 17 5 40 59 32 62 6 0 -41 13 58 7 54 84 8 84 27 55 1 0 -24 80 44 26 86 99 68 80 81 22 9 0 -12 45 16 44 66 76 33 53 3 20 9 0 -22 3 79 6 32 38 75 66 15 25 9 1 -51 48 26 53 33 26 18 74 9 39 5 1 -35 67 89 91 29 81 23 52 19 11 6 0 -64 50 43 1 43 49 19 20 84 19 8 0 -34 4 9 77 24 61 55 82 42 76 9 0 -37 84 94 33 67 60 3 95 78 8 9 0 -82 10 54 12 47 23 78 97 6 51 5 0 -70 40 38 47 5 38 83 70 37 90 2 0 -42 21 62 27 43 47 82 80 88 49 4 0 -68 68 67 12 38 13 32 30 93 27 3 1 - 5 44 98 28 5 81 20 56 10 34 9 1 -40 46 11 33 73 62 68 70 66 85 4 0 - 9 46 11 84 6 31 18 89 66 32 1 1 - 6 78 44 98 77 29 69 39 62 78 1 0 -47 90 18 0 3 8 12 20 51 75 4 1 -21 29 74 19 12 29 41 22 63 47 8 1 -22 59 64 62 18 89 19 92 87 8 8 0 - 6 21 24 58 14 53 18 93 62 15 8 0 -20 33 88 25 37 52 1 72 74 11 2 0 -90 49 28 53 28 80 22 81 0 46 9 0 -87 31 51 27 15 31 68 93 5 4 7 1 -21 72 60 2 24 79 22 24 77 61 9 0 -20 4 6 40 28 14 16 78 58 99 7 1 -80 35 98 20 91 35 47 29 3 19 2 1 -57 21 24 61 60 39 83 34 53 2 2 0 -74 86 78 78 18 44 20 94 85 71 4 1 -27 48 44 92 10 18 74 54 25 85 2 0 -74 77 28 75 74 91 69 36 95 68 7 0 -32 84 17 18 55 79 59 57 21 69 2 1 -69 77 40 98 83 40 4 66 39 83 1 1 -63 24 32 39 75 92 81 49 2 51 5 1 -35 40 84 71 3 16 82 91 44 52 8 0 -21 78 66 4 57 27 21 89 4 34 7 1 -94 18 57 49 88 26 29 76 56 67 6 0 -14 91 71 30 5 36 28 74 16 73 3 1 -93 36 43 46 77 44 59 19 56 84 3 0 -11 16 2 67 11 96 20 91 20 59 2 1 -72 79 26 99 90 71 56 46 35 99 3 0 -29 87 20 40 13 14 14 40 61 27 6 0 -41 64 28 51 56 52 87 67 37 91 6 1 -33 14 5 30 99 54 27 80 54 55 4 1 -60 44 73 91 71 53 54 95 59 81 6 0 -69 33 11 83 4 53 34 39 43 84 1 0 -73 31 19 4 50 20 66 73 94 88 4 0 -30 49 41 76 5 21 88 69 76 3 2 0 -18 50 27 76 67 38 87 16 52 87 5 1 -33 36 80 8 43 82 89 76 37 3 5 0 -98 21 61 24 58 13 9 85 56 74 1 1 -84 27 50 96 9 56 30 31 85 65 1 1 -65 74 40 2 8 40 18 57 30 38 1 1 -76 44 64 6 10 32 84 70 74 24 1 1 -14 29 59 34 27 8 0 37 27 68 3 0 - 6 47 5 77 15 41 93 49 59 83 4 1 -39 88 43 89 32 98 82 0 5 12 9 0 -78 79 30 26 58 6 9 58 37 65 8 1 -25 28 66 41 70 87 76 62 29 39 7 1 diff --git a/models/contentunderstanding/classification/model.py b/models/contentunderstanding/classification/model.py index cb84e7385471a114e5235404bee019ac4e5f6477..2347646cda2a6b7805bf9cb7c5e5fbb189af7b53 100644 --- a/models/contentunderstanding/classification/model.py +++ b/models/contentunderstanding/classification/model.py @@ -20,28 +20,32 @@ from paddlerec.core.model import ModelBase class Model(ModelBase): def __init__(self, config): ModelBase.__init__(self, config) - self.dict_dim = 100 - self.max_len = 10 - self.cnn_dim = 32 - self.cnn_filter_size = 128 - self.emb_dim = 8 - self.hid_dim = 128 - self.class_dim = 2 - self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", - False) + self.dict_dim = envs.get_global_env("hyper_parameters.dict_dim") + self.max_len = envs.get_global_env("hyper_parameters.max_len") + self.cnn_dim = envs.get_global_env("hyper_parameters.cnn_dim") + self.cnn_filter_size1 = envs.get_global_env( + "hyper_parameters.cnn_filter_size1") + self.cnn_filter_size2 = envs.get_global_env( + "hyper_parameters.cnn_filter_size2") + self.cnn_filter_size3 = envs.get_global_env( + "hyper_parameters.cnn_filter_size3") + self.emb_dim = envs.get_global_env("hyper_parameters.emb_dim") + self.hid_dim = envs.get_global_env("hyper_parameters.hid_dim") + self.class_dim = envs.get_global_env("hyper_parameters.class_dim") + self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse") def input_data(self, is_infer=False, **kwargs): data = fluid.data( name="input", shape=[None, self.max_len], dtype='int64') - label = fluid.data(name="label", shape=[None, 1], dtype='int64') seq_len = fluid.data(name="seq_len", shape=[None], dtype='int64') - return [data, label, seq_len] + label = fluid.data(name="label", shape=[None, 1], dtype='int64') + return [data, seq_len, label] def net(self, input, is_infer=False): """ network definition """ data = input[0] - label = input[1] - seq_len = input[2] + seq_len = input[1] + label = input[2] # embedding layer emb = fluid.embedding( @@ -50,15 +54,31 @@ class Model(ModelBase): is_sparse=self.is_sparse) emb = fluid.layers.sequence_unpad(emb, length=seq_len) # convolution layer - conv = fluid.nets.sequence_conv_pool( + 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_size, + 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=[conv], size=self.hid_dim) + 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, @@ -70,5 +90,7 @@ class Model(ModelBase): 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 diff --git a/models/contentunderstanding/classification/reader.py b/models/contentunderstanding/classification/reader.py index 1e782cca99b5980e2cce45ebf13018ea74e8e21d..810c5d08698467a42b7b0b46752ff0657b2049c7 100644 --- a/models/contentunderstanding/classification/reader.py +++ b/models/contentunderstanding/classification/reader.py @@ -23,9 +23,10 @@ class Reader(ReaderBase): def _process_line(self, l): l = l.strip().split() - data = l[0:10] - seq_len = l[10:11] - label = l[11:] + data = l[0:100] + seq_len = l[100:101] + label = l[101:] + return data, label, seq_len def generate_sample(self, line): @@ -37,6 +38,6 @@ class Reader(ReaderBase): 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), ('label', label), ('seq_len', seq_len)] + yield [('data', data), ('seq_len', seq_len), ('label', label)] return data_iter diff --git a/models/contentunderstanding/classification/readme.md b/models/contentunderstanding/classification/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..1326a55d9cbe9650b45ea99db8569bd7ef13b3a7 --- /dev/null +++ b/models/contentunderstanding/classification/readme.md @@ -0,0 +1,131 @@ +# classification文本分类模型 + +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 +├── 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) + + +## 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 +TextCNN网络是2014年提出的用来做文本分类的卷积神经网络,由于其结构简单、效果好,在文本分类、推荐等NLP领域应用广泛。对于文本分类问题,常见的方法无非就是抽取文本的特征。然后再基于抽取的特征训练一个分类器。 然而研究证明,TextCnn在文本分类问题上有着更加卓越的表现。从直观上理解,TextCNN通过一维卷积来获取句子中n-gram的特征表示。TextCNN对文本浅层特征的抽取能力很强,在短文本领域专注于意图分类时效果很好,应用广泛,且速度较快。 +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: +
+ +
+ +## 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,我们基于开源情感倾向分类数据集ChnSentiCorp进行评测,模型在测试集上的准确率如表所示: + +| 模型 | dev | test | +| :------| :------ | :------ +| TextCNN | 90.75% | 92.19% | + + +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 +``` + + +## 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 +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 +tar -zxvf sentiment_classification-dataset-1.0.0.tar.gz +``` +3. 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,您在解压数据集后,将preprocess.py复制到senta_data文件中并执行,即可将数据集中提供的dev.tsv,test.tsv,train.tsv转化为可直接训练的dev.txt,test.txt,train.txt. +``` +cp ./data/preprocess.py ./senta_data/ +cd senta_data/ +python preprocess.py +``` +4. 创建存放训练集和测试集的目录,将数据放入目录中。 +``` +mkdir train +mv train.txt train +mkdir test +mv dev.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) +将data1下的batch_size值从10改为128 +将data1下的data_path改为:{workspace}/senta_data/train +将dataset_infer下的batch_size值从2改为256 +将dataset_infer下的data_path改为:{workspace}/senta_data/test + +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/14 +batch: 1, acc: [0.91796875], loss: [0.2287855] +batch: 2, acc: [0.91796875], loss: [0.22827303] +batch: 3, acc: [0.90234375], loss: [0.27907994] +``` +## 进阶使用 + +## FAQ diff --git a/models/contentunderstanding/readme.md b/models/contentunderstanding/readme.md index ba63b620886c5a9346b1e5bb9d69e0aa15336df9..91d2a2db335fb45ef04d62db13f692aff5b2e500 100644 --- a/models/contentunderstanding/readme.md +++ b/models/contentunderstanding/readme.md @@ -22,12 +22,12 @@ | 模型 | 简介 | 论文 | | :------------------: | :--------------------: | :---------: | -| TagSpace | 标签推荐 | [EMNLP 2014][TagSpace: Semantic Embeddings from Hashtags](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags/) | +| 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) | 下面是每个模型的简介(注:图片引用自链接中的论文) -[TagSpace模型](https://research.fb.com/publications/tagspace-semantic-embeddings-from-hashtags) +[TagSpace模型](https://www.aclweb.org/anthology/D14-1194.pdf)
@@ -37,89 +37,173 @@
-##使用教程(快速开始) +## 使用教程(快速开始) ``` git clone https://github.com/PaddlePaddle/PaddleRec.git paddle-rec -cd paddle-rec - +cd PaddleRec python -m paddlerec.run -m models/contentunderstanding/tagspace/config.yaml python -m paddlerec.run -m models/contentunderstanding/classification/config.yaml ``` ## 使用教程(复现论文) -###注意 +### 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。如果需要复现readme中的效果请使用以下提供的脚本下载对应数据集以及数据预处理。 -### 数据处理 **(1)TagSpace** +### 数据处理 [数据地址](https://github.com/mhjabreel/CharCNN/tree/master/data/) , [备份数据地址](https://paddle-tagspace.bj.bcebos.com/data.tar) - + 数据格式如下 ``` "3","Wall St. Bears Claw Back Into the Black (Reuters)","Reuters - Short-sellers, Wall Street's dwindling\band of ultra-cynics, are seeing green again." ``` -数据解压后,将文本数据转为paddle数据,先将数据放到训练数据目录和测试数据目录 - +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,您在解压数据集后,将原始数据存放在raw_big_train_data和raw_big_test_data两个目录下,并在python3环境下运行我们提供的text2paddle.py文件。即可生成可以直接用于训练的数据目录test_big_data和train_big_data。命令如下: ``` mkdir raw_big_train_data mkdir raw_big_test_data mv train.csv raw_big_train_data mv test.csv raw_big_test_data +python3 text2paddle.py raw_big_train_data/ raw_big_test_data/ train_big_data test_big_data big_vocab_text.txt big_vocab_tag.txt ``` -运行脚本text2paddle.py 生成paddle输入格式 +运行后的data目录: + +``` +big_vocab_tag.txt #标签词汇数 +big_vocab_text.txt #文本词汇数 +data.tar #数据集 +raw_big_train_data #数据集中原始的训练集 +raw_big_test_data #数据集中原始的测试集 +train_data #样例训练集 +test_data #样例测试集 +train_big_data #数据集经处理后的训练集 +test_big_data #数据集经处理后的测试集 +text2paddle.py #预处理文件 +``` +处理完成的数据格式如下: ``` -python text2paddle.py raw_big_train_data/ raw_big_test_data/ train_big_data test_big_data big_vocab_text.txt big_vocab_tag.txt +2,27 7062 8390 456 407 8 11589 3166 4 7278 31046 33 3898 2897 426 1 +2,27 9493 836 355 20871 300 81 19 3 4125 9 449 462 13832 6 16570 1380 2874 5 0 797 236 19 3688 2106 14 8615 7 209 304 4 0 123 1 +2,27 12754 637 106 3839 1532 66 0 379 6 0 1246 9 307 33 161 2 8100 36 0 350 123 101 74 181 0 6657 4 0 1222 17195 1 ``` + ### 训练 +退回tagspace目录中,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) +将dataset下sample_1的batch_size值从10改为128 +将dataset下sample_1的data_path改为:{workspace}/data/train_big_data +将dataset下inferdata的batch_size值从10改为500 +将dataset下inferdata的data_path改为:{workspace}/data/test_big_data +执行命令,开始训练: ``` -cd modles/contentunderstanding/tagspace -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 +python -m paddlerec.run -m ./config.yaml ``` ### 预测 +在跑完训练后,模型会开始在验证集上预测。 +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/9 +batch: 1, acc: [0.91], loss: [0.02495437] +batch: 2, acc: [0.936], loss: [0.01941476] +batch: 3, acc: [0.918], loss: [0.02116447] +batch: 4, acc: [0.916], loss: [0.0219945] +batch: 5, acc: [0.902], loss: [0.02242816] +batch: 6, acc: [0.9], loss: [0.02421589] +batch: 7, acc: [0.9], loss: [0.026441] +batch: 8, acc: [0.934], loss: [0.01797657] +batch: 9, acc: [0.932], loss: [0.01687362] +batch: 10, acc: [0.926], loss: [0.02047823] +batch: 11, acc: [0.918], loss: [0.01998716] +batch: 12, acc: [0.898], loss: [0.0229556] +batch: 13, acc: [0.928], loss: [0.01736144] +batch: 14, acc: [0.93], loss: [0.01911209] ``` -# 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: 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 +**(2)Classification** + +### 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。 +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 ``` -**(2)Classification** +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。 -### 训练 ``` -cd modles/contentunderstanding/classification -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 +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 +``` +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,您在解压数据集后,将preprocess.py复制到senta_data文件中并执行,即可将数据集中提供的dev.tsv,test.tsv,train.tsv转化为可直接训练的dev.txt,test.txt,train.txt. +``` +cp ./data/preprocess.py ./senta_data/ +cd senta_data/ +python preprocess.py ``` -### 预测 +### 训练 +创建存放训练集和测试集的目录,将数据放入目录中。 ``` -# 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: infer -# 修改phase阶段为infer的配置,参照config注释 +mkdir train +mv train.txt train +mkdir test +mv dev.txt test +cd .. +``` + +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) +将data1下的batch_size值从10改为128 +将data1下的data_path改为:{workspace}/senta_data/train +将dataset_infer下的batch_size值从2改为256 +将dataset_infer下的data_path改为:{workspace}/senta_data/test -# 修改完config.yaml后 执行: +执行命令,开始训练: +``` python -m paddlerec.run -m ./config.yaml ``` +### 预测 +在跑完训练后,模型会开始在验证集上预测。 +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/14 +batch: 1, acc: [0.91796875], loss: [0.2287855] +batch: 2, acc: [0.91796875], loss: [0.22827303] +batch: 3, acc: [0.90234375], loss: [0.27907994] +``` + + ## 效果对比 ### 模型效果 (测试) -| 数据集 | 模型 | loss | auc | acc | mae | -| :------------------: | :--------------------: | :---------: |:---------: | :---------: |:---------: | -| ag news dataset | TagSpace | -- | -- | -- | -- | -| -- | Classification | -- | -- | -- | -- | +| 数据集 | 模型 | loss | acc | +| :------------------: | :--------------------: | :---------: |:---------: | +| ag news dataset | TagSpace | 0.0198 | 0.9177 | +| ChnSentiCorp | Classification | 0.2282 | 0.9127 | diff --git a/models/contentunderstanding/tagspace/config.yaml b/models/contentunderstanding/tagspace/config.yaml index 84c9ac82e5d427d9433499b030cf9d372086c2cb..994676ef716c379db9373a7461f5c7a2f28a2c87 100644 --- a/models/contentunderstanding/tagspace/config.yaml +++ b/models/contentunderstanding/tagspace/config.yaml @@ -16,16 +16,21 @@ workspace: "models/contentunderstanding/tagspace" dataset: - name: sample_1 - type: QueueDataset - batch_size: 5 + type: DataLoader + batch_size: 10 data_path: "{workspace}/data/train_data" data_converter: "{workspace}/reader.py" +- name: inferdata + type: DataLoader + batch_size: 10 + data_path: "{workspace}/data/test_data" + data_converter: "{workspace}/reader.py" hyper_parameters: optimizer: class: Adagrad learning_rate: 0.001 - vocab_text_size: 11447 + vocab_text_size: 75378 vocab_tag_size: 4 emb_dim: 10 hid_dim: 1000 @@ -34,22 +39,34 @@ hyper_parameters: neg_size: 3 num_devices: 1 -mode: runner1 +mode: [runner1,infer_runner] runner: - name: runner1 class: train epochs: 10 device: cpu - save_checkpoint_interval: 2 - save_inference_interval: 4 + save_checkpoint_interval: 1 + save_inference_interval: 1 save_checkpoint_path: "increment" save_inference_path: "inference" save_inference_feed_varnames: [] save_inference_fetch_varnames: [] + phases: phase1 +- name: infer_runner + class: infer + # device to run training or infer + device: cpu + print_interval: 1 + init_model_path: "increment/9" # load model path + phases: phase_infer phase: - name: phase1 model: "{workspace}/model.py" dataset_name: sample_1 thread_num: 1 +- name: phase_infer + model: "{workspace}/model.py" + dataset_name: inferdata + thread_num: 1 diff --git a/models/contentunderstanding/tagspace/data/text2paddle.py b/models/contentunderstanding/tagspace/data/text2paddle.py new file mode 100644 index 0000000000000000000000000000000000000000..9b4a4fafb2945bfb30869dafb86e066096a8bea0 --- /dev/null +++ b/models/contentunderstanding/tagspace/data/text2paddle.py @@ -0,0 +1,126 @@ +# 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 +import six +import collections +import os +import csv +import re +import sys +if six.PY2: + reload(sys) + sys.setdefaultencoding('utf-8') + + +def word_count(column_num, input_file, word_freq=None): + """ + compute word count from corpus + """ + if word_freq is None: + word_freq = collections.defaultdict(int) + data_file = csv.reader(input_file) + for row in data_file: + for w in re.split(r'\W+', row[column_num].strip()): + word_freq[w] += 1 + return word_freq + + +def build_dict(column_num=2, min_word_freq=0, train_dir="", test_dir=""): + """ + Build a word dictionary from the corpus, Keys of the dictionary are words, + and values are zero-based IDs of these words. + """ + word_freq = collections.defaultdict(int) + files = os.listdir(train_dir) + for fi in files: + with open(os.path.join(train_dir, fi), "r", encoding='utf-8') as f: + word_freq = word_count(column_num, f, word_freq) + files = os.listdir(test_dir) + for fi in files: + with open(os.path.join(test_dir, fi), "r", encoding='utf-8') as f: + word_freq = word_count(column_num, f, word_freq) + + word_freq = [x for x in six.iteritems(word_freq) if x[1] > min_word_freq] + word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0])) + words, _ = list(zip(*word_freq_sorted)) + word_idx = dict(list(zip(words, six.moves.range(len(words))))) + return word_idx + + +def write_paddle(text_idx, tag_idx, train_dir, test_dir, output_train_dir, + output_test_dir): + files = os.listdir(train_dir) + if not os.path.exists(output_train_dir): + os.mkdir(output_train_dir) + for fi in files: + with open(os.path.join(train_dir, fi), "r", encoding='utf-8') as f: + with open( + os.path.join(output_train_dir, fi), "w", + encoding='utf-8') as wf: + data_file = csv.reader(f) + for row in data_file: + tag_raw = re.split(r'\W+', row[0].strip()) + pos_index = tag_idx.get(tag_raw[0]) + wf.write(str(pos_index) + ",") + text_raw = re.split(r'\W+', row[2].strip()) + l = [text_idx.get(w) for w in text_raw] + for w in l: + wf.write(str(w) + " ") + wf.write("\n") + + files = os.listdir(test_dir) + if not os.path.exists(output_test_dir): + os.mkdir(output_test_dir) + for fi in files: + with open(os.path.join(test_dir, fi), "r", encoding='utf-8') as f: + with open( + os.path.join(output_test_dir, fi), "w", + encoding='utf-8') as wf: + data_file = csv.reader(f) + for row in data_file: + tag_raw = re.split(r'\W+', row[0].strip()) + pos_index = tag_idx.get(tag_raw[0]) + wf.write(str(pos_index) + ",") + text_raw = re.split(r'\W+', row[2].strip()) + l = [text_idx.get(w) for w in text_raw] + for w in l: + wf.write(str(w) + " ") + wf.write("\n") + + +def text2paddle(train_dir, test_dir, output_train_dir, output_test_dir, + output_vocab_text, output_vocab_tag): + print("start constuct word dict") + vocab_text = build_dict(2, 0, train_dir, test_dir) + with open(output_vocab_text, "w", encoding='utf-8') as wf: + wf.write(str(len(vocab_text)) + "\n") + + vocab_tag = build_dict(0, 0, train_dir, test_dir) + with open(output_vocab_tag, "w", encoding='utf-8') as wf: + wf.write(str(len(vocab_tag)) + "\n") + + print("construct word dict done\n") + write_paddle(vocab_text, vocab_tag, train_dir, test_dir, output_train_dir, + output_test_dir) + + +train_dir = sys.argv[1] +test_dir = sys.argv[2] +output_train_dir = sys.argv[3] +output_test_dir = sys.argv[4] +output_vocab_text = sys.argv[5] +output_vocab_tag = sys.argv[6] +text2paddle(train_dir, test_dir, output_train_dir, output_test_dir, + output_vocab_text, output_vocab_tag) diff --git a/models/contentunderstanding/tagspace/model.py b/models/contentunderstanding/tagspace/model.py index 8ee26ee4f7a2f7b21cc565eb27d8a0a6e55b4080..d07199233f0b4cb7895cda86f1a971278a4a0585 100644 --- a/models/contentunderstanding/tagspace/model.py +++ b/models/contentunderstanding/tagspace/model.py @@ -16,7 +16,6 @@ import paddle.fluid as fluid import paddle.fluid.layers.nn as nn import paddle.fluid.layers.tensor as tensor import paddle.fluid.layers.control_flow as cf - from paddlerec.core.model import ModelBase from paddlerec.core.utils import envs @@ -98,14 +97,19 @@ class Model(ModelBase): tensor.fill_constant_batch_size_like( input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'), loss_part2) - avg_cost = nn.mean(loss_part3) + avg_cost = fluid.layers.mean(loss_part3) + less = tensor.cast(cf.less_than(cos_neg, cos_pos), dtype='float32') + label_ones = fluid.layers.fill_constant_batch_size_like( + input=cos_neg, dtype='float32', shape=[-1, 1], value=1.0) correct = nn.reduce_sum(less) + total = fluid.layers.reduce_sum(label_ones) + acc = fluid.layers.elementwise_div(correct, total) self._cost = avg_cost if is_infer: - self._infer_results["correct"] = correct - self._infer_results["cos_pos"] = cos_pos + self._infer_results["acc"] = acc + self._infer_results["loss"] = self._cost else: - self._metrics["correct"] = correct - self._metrics["cos_pos"] = cos_pos + self._metrics["acc"] = acc + self._metrics["loss"] = self._cost diff --git a/models/contentunderstanding/tagspace/readme.md b/models/contentunderstanding/tagspace/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..49647635e4508ec879ec55dc950b052b23db0bf1 --- /dev/null +++ b/models/contentunderstanding/tagspace/readme.md @@ -0,0 +1,147 @@ +# tagspace文本分类模型 + +以下是本例的简要目录结构及说明: + +``` +├── data #样例数据 + ├── train_data + ├── small_train.csv #训练数据样例 + ├── test_data + ├── small_test.csv #测试数据样例 + ├── text2paddle.py #数据处理程序 +├── __init__.py +├── README.md #文档 +├── model.py #模型文件 +├── 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) + +## 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 +tagspace模型是一种对文本打标签的方法,它主要学习从短文到相关主题标签的映射。论文中主要利用CNN做doc向量, 然后优化 f(w,t+),f(w,t-)的距离作为目标函数,得到了 t(标签)和doc在一个特征空间的向量表达,这样就可以找 doc的hashtags了。 + +论文[TAGSPACE: Semantic Embeddings from Hashtags](https://www.aclweb.org/anthology/D14-1194.pdf)中的网络结构如图所示,一层输入层,一个卷积层,一个pooling层以及最后一个全连接层进行降维。 +
+ +
+ +## 数据准备 +[数据地址](https://github.com/mhjabreel/CharCNN/tree/master/data/) , [备份数据地址](https://paddle-tagspace.bj.bcebos.com/data.tar) + +数据格式如下: +``` +"3","Wall St. Bears Claw Back Into the Black (Reuters)","Reuters - Short-sellers, Wall Street's dwindling\band of ultra-cynics, are seeing green again." +``` + +## 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/tagspace/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/tagspace +2. 在data目录下载并解压数据集,命令如下: +``` +cd data +wget https://paddle-tagspace.bj.bcebos.com/data.tar +tar -xvf data.tar +``` +3. 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,您在解压数据集后,将原始数据存放在raw_big_train_data和raw_big_test_data两个目录下,并在python3环境下运行我们提供的text2paddle.py文件。即可生成可以直接用于训练的数据目录test_big_data和train_big_data。命令如下: +``` +mkdir raw_big_train_data +mkdir raw_big_test_data +mv train.csv raw_big_train_data +mv test.csv raw_big_test_data +python3 text2paddle.py raw_big_train_data/ raw_big_test_data/ train_big_data test_big_data big_vocab_text.txt big_vocab_tag.txt +``` + +运行后的data目录: + +``` +big_vocab_tag.txt #标签词汇数 +big_vocab_text.txt #文本词汇数 +data.tar #数据集 +raw_big_train_data #数据集中原始的训练集 +raw_big_test_data #数据集中原始的测试集 +train_data #样例训练集 +test_data #样例测试集 +train_big_data #数据集经处理后的训练集 +test_big_data #数据集经处理后的测试集 +text2paddle.py #预处理文件 +``` + +处理完成的数据格式如下: +``` +2,27 7062 8390 456 407 8 11589 3166 4 7278 31046 33 3898 2897 426 1 +2,27 9493 836 355 20871 300 81 19 3 4125 9 449 462 13832 6 16570 1380 2874 5 0 797 236 19 3688 2106 14 8615 7 209 304 4 0 123 1 +2,27 12754 637 106 3839 1532 66 0 379 6 0 1246 9 307 33 161 2 8100 36 0 350 123 101 74 181 0 6657 4 0 1222 17195 1 +``` + +4. 退回tagspace目录中,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) +将dataset下sample_1的batch_size值从10改为128 +将dataset下sample_1的data_path改为:{workspace}/data/train_big_data +将dataset下inferdata的batch_size值从10改为500 +将dataset下inferdata的data_path改为:{workspace}/data/test_big_data + +5. 执行命令,开始训练: +``` +python -m paddlerec.run -m ./config.yaml +``` +6. 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/9 +batch: 1, acc: [0.91], loss: [0.02495437] +batch: 2, acc: [0.936], loss: [0.01941476] +batch: 3, acc: [0.918], loss: [0.02116447] +batch: 4, acc: [0.916], loss: [0.0219945] +batch: 5, acc: [0.902], loss: [0.02242816] +batch: 6, acc: [0.9], loss: [0.02421589] +batch: 7, acc: [0.9], loss: [0.026441] +batch: 8, acc: [0.934], loss: [0.01797657] +batch: 9, acc: [0.932], loss: [0.01687362] +batch: 10, acc: [0.926], loss: [0.02047823] +batch: 11, acc: [0.918], loss: [0.01998716] +batch: 12, acc: [0.898], loss: [0.0229556] +batch: 13, acc: [0.928], loss: [0.01736144] +batch: 14, acc: [0.93], loss: [0.01911209] +``` + +## 进阶使用 + +## FAQ diff --git a/models/match/dssm/config.yaml b/models/match/dssm/config.yaml index 6c0d61a2676a57f400647a1bcdc9ca6d8d6fdb1e..2f055aee8a6c4ad7de485a935cf74a17ceae0ef9 100755 --- a/models/match/dssm/config.yaml +++ b/models/match/dssm/config.yaml @@ -17,50 +17,52 @@ workspace: "models/match/dssm" dataset: - name: dataset_train - batch_size: 4 - type: QueueDataset + batch_size: 8 + type: DataLoader # or QueueDataset data_path: "{workspace}/data/train" data_converter: "{workspace}/synthetic_reader.py" - name: dataset_infer batch_size: 1 - type: QueueDataset - data_path: "{workspace}/data/train" + type: DataLoader # or QueueDataset + data_path: "{workspace}/data/test" data_converter: "{workspace}/synthetic_evaluate_reader.py" hyper_parameters: optimizer: class: sgd - learning_rate: 0.01 + learning_rate: 0.001 strategy: async - trigram_d: 1000 - neg_num: 4 + trigram_d: 1439 + neg_num: 1 fc_sizes: [300, 300, 128] fc_acts: ['tanh', 'tanh', 'tanh'] -mode: train_runner +mode: [train_runner,infer_runner] # config of each runner. # runner is a kind of paddle training class, which wraps the train/infer process. runner: - name: train_runner class: train # num of epochs - epochs: 4 + epochs: 3 # device to run training or infer device: cpu - save_checkpoint_interval: 2 # save model interval of epochs - save_inference_interval: 4 # save inference + save_checkpoint_interval: 1 # save model interval of epochs + save_inference_interval: 1 # save inference save_checkpoint_path: "increment" # save checkpoint path save_inference_path: "inference" # save inference path save_inference_feed_varnames: ["query", "doc_pos"] # feed vars of save inference save_inference_fetch_varnames: ["cos_sim_0.tmp_0"] # fetch vars of save inference init_model_path: "" # load model path print_interval: 2 + phases: phase1 - name: infer_runner class: infer # device to run training or infer device: cpu print_interval: 1 init_model_path: "increment/2" # load model path + phases: phase2 # runner will run all the phase in each epoch phase: @@ -68,7 +70,7 @@ phase: model: "{workspace}/model.py" # user-defined model dataset_name: dataset_train # select dataset by name thread_num: 1 -#- name: phase2 -# model: "{workspace}/model.py" # user-defined model -# dataset_name: dataset_infer # select dataset by name -# thread_num: 1 +- name: phase2 + model: "{workspace}/model.py" # user-defined model + dataset_name: dataset_infer # select dataset by name + thread_num: 1 diff --git a/models/match/dssm/data/preprocess.py b/models/match/dssm/data/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..7594e4fe1e4b07992be5055e8e1fcfca63746c4e --- /dev/null +++ b/models/match/dssm/data/preprocess.py @@ -0,0 +1,125 @@ +# 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. +#encoding=utf-8 + +import os +import sys +import numpy as np +import random + +f = open("./zhidao", "r") +lines = f.readlines() +f.close() + +#建立字典 +word_dict = {} +for line in lines: + line = line.strip().split("\t") + text = line[0].split(" ") + line[1].split(" ") + for word in text: + if word in word_dict: + continue + else: + word_dict[word] = len(word_dict) + 1 + +f = open("./zhidao", "r") +lines = f.readlines() +f.close() + +lines = [line.strip().split("\t") for line in lines] + +#建立以query为key,以负例为value的字典 +neg_dict = {} +for line in lines: + if line[2] == "0": + if line[0] in neg_dict: + neg_dict[line[0]].append(line[1]) + else: + neg_dict[line[0]] = [line[1]] + +#建立以query为key,以正例为value的字典 +pos_dict = {} +for line in lines: + if line[2] == "1": + if line[0] in pos_dict: + pos_dict[line[0]].append(line[1]) + else: + pos_dict[line[0]] = [line[1]] + +#划分训练集和测试集 +query_list = list(pos_dict.keys()) +#print(len(query)) +random.shuffle(query_list) +train_query = query_list[:90] +test_query = query_list[90:] + +#获得训练集 +train_set = [] +for query in train_query: + for pos in pos_dict[query]: + if query not in neg_dict: + continue + for neg in neg_dict[query]: + train_set.append([query, pos, neg]) +random.shuffle(train_set) + +#获得测试集 +test_set = [] +for query in test_query: + for pos in pos_dict[query]: + test_set.append([query, pos, 1]) + if query not in neg_dict: + continue + for neg in neg_dict[query]: + test_set.append([query, neg, 0]) +random.shuffle(test_set) + +#训练集中的query,pos,neg转化为词袋 +f = open("train.txt", "w") +for line in train_set: + query = line[0].strip().split(" ") + pos = line[1].strip().split(" ") + neg = line[2].strip().split(" ") + query_token = [0] * (len(word_dict) + 1) + for word in query: + query_token[word_dict[word]] = 1 + pos_token = [0] * (len(word_dict) + 1) + for word in pos: + pos_token[word_dict[word]] = 1 + neg_token = [0] * (len(word_dict) + 1) + for word in neg: + neg_token[word_dict[word]] = 1 + f.write(','.join([str(x) for x in query_token]) + "\t" + ','.join([ + str(x) for x in pos_token + ]) + "\t" + ','.join([str(x) for x in neg_token]) + "\n") +f.close() + +#测试集中的query和pos转化为词袋 +f = open("test.txt", "w") +fa = open("label.txt", "w") +for line in test_set: + query = line[0].strip().split(" ") + pos = line[1].strip().split(" ") + label = line[2] + query_token = [0] * (len(word_dict) + 1) + for word in query: + query_token[word_dict[word]] = 1 + pos_token = [0] * (len(word_dict) + 1) + for word in pos: + pos_token[word_dict[word]] = 1 + f.write(','.join([str(x) for x in query_token]) + "\t" + ','.join( + [str(x) for x in pos_token]) + "\n") + fa.write(str(label) + "\n") +f.close() +fa.close() diff --git a/models/match/dssm/data/test/test.txt b/models/match/dssm/data/test/test.txt new file mode 100644 index 0000000000000000000000000000000000000000..67bfe1a45da45ec3c2f9cf25552c299cba76bc0d --- /dev/null +++ b/models/match/dssm/data/test/test.txt @@ -0,0 +1,100 @@ 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diff --git a/models/match/dssm/data/train/sample_train.txt b/models/match/dssm/data/train/sample_train.txt deleted file mode 100755 index 11eec4f49ff6b1c1d4b2e8a876b564acd86583d4..0000000000000000000000000000000000000000 --- a/models/match/dssm/data/train/sample_train.txt +++ /dev/null @@ -1,16 +0,0 @@ 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diff --git a/models/match/dssm/model.py b/models/match/dssm/model.py index 46e9715bc1f68472c831fe661796b3ae27cb0fbe..7b0164971ff7f91eea5c5413ec7941690a1a7ebe 100755 --- a/models/match/dssm/model.py +++ b/models/match/dssm/model.py @@ -73,6 +73,7 @@ class Model(ModelBase): query_fc = fc(inputs[0], self.hidden_layers, self.hidden_acts, ['query_l1', 'query_l2', 'query_l3']) + doc_pos_fc = fc(inputs[1], self.hidden_layers, self.hidden_acts, ['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3']) R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc) @@ -93,7 +94,7 @@ class Model(ModelBase): prob = fluid.layers.softmax(concat_Rs, axis=1) hit_prob = fluid.layers.slice( - prob, axes=[0, 1], starts=[0, 0], ends=[4, 1]) + prob, axes=[0, 1], starts=[0, 0], ends=[8, 1]) loss = -fluid.layers.reduce_sum(fluid.layers.log(hit_prob)) avg_cost = fluid.layers.mean(x=loss) self._cost = avg_cost diff --git a/models/match/dssm/readme.md b/models/match/dssm/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..c3d8799ce9e3496e265009bd51ab2f5f3f35aebc --- /dev/null +++ b/models/match/dssm/readme.md @@ -0,0 +1,181 @@ +# DSSM文本匹配模型 + +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 +├── config.yaml #配置文件 +├── synthetic_reader.py #读取训练集的程序 +├── synthetic_evaluate_reader.py #读取测试集的程序 +├── transform.py #将数据整理成合适的格式方便计算指标 +├── run.sh #全量数据集中的训练脚本,从训练到预测并计算指标 + +``` + +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 +DSSM是Deep Structured Semantic Model的缩写,即我们通常说的基于深度网络的语义模型,其核心思想是将query和doc映射到到共同维度的语义空间中,通过最大化query和doc语义向量之间的余弦相似度,从而训练得到隐含语义模型,达到检索的目的。DSSM有很广泛的应用,比如:搜索引擎检索,广告相关性,问答系统,机器翻译等。 +DSSM 的输入采用 BOW(Bag of words)的方式,相当于把字向量的位置信息抛弃了,整个句子里的词都放在一个袋子里了。将一个句子用这种方式转化为一个向量输入DNN中。 +Query 和 Doc 的语义相似性可以用这两个向量的 cosine 距离表示,然后通过softmax 函数选出与Query语义最相似的样本 Doc 。 + +模型的具体细节可以阅读论文[DSSM](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf): +
+ +
+ +## 数据准备 +我们公开了自建的测试集,包括百度知道、ECOM、QQSIM、UNICOM 四个数据集。这里我们选取百度知道数据集来进行训练。执行以下命令可以获取上述数据集。 +``` +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz +tar xzf simnet_dataset-1.0.0.tar.gz +rm simnet_dataset-1.0.0.tar.gz +``` + +## 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 +本文提供了样例数据可以供您快速体验,在paddlerec目录下执行下面的命令即可快速启动训练: + +``` +python -m paddlerec.run -m models/match/dssm/config.yaml +``` + +输出结果示例: +``` +PaddleRec: Runner train_runner Begin +Executor Mode: train +processor_register begin +Running SingleInstance. +Running SingleNetwork. +file_list : ['models/match/dssm/data/train/train.txt'] +Running SingleStartup. +Running SingleRunner. +!!! The CPU_NUM is not specified, you should set CPU_NUM in the environment variable list. +CPU_NUM indicates that how many CPUPlace are used in the current task. +And if this parameter are set as N (equal to the number of physical CPU core) the program may be faster. +export CPU_NUM=32 # for example, set CPU_NUM as number of physical CPU core which is 32. +!!! The default number of CPU_NUM=1. +I0821 06:56:26.224299 31061 parallel_executor.cc:440] The Program will be executed on CPU using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. +I0821 06:56:26.231163 31061 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 +I0821 06:56:26.237023 31061 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True +I0821 06:56:26.240788 31061 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0 +batch: 2, LOSS: [4.538238] +batch: 4, LOSS: [4.16424] +batch: 6, LOSS: [3.8121371] +batch: 8, LOSS: [3.4250507] +batch: 10, LOSS: [3.2285979] +batch: 12, LOSS: [3.2116117] +batch: 14, LOSS: [3.1406002] +epoch 0 done, use time: 0.357971906662, global metrics: LOSS=[3.0968776] +batch: 2, LOSS: [2.6843479] +batch: 4, LOSS: [2.546976] +batch: 6, LOSS: [2.4103594] +batch: 8, LOSS: [2.301374] +batch: 10, LOSS: [2.264183] +batch: 12, LOSS: [2.315862] +batch: 14, LOSS: [2.3409634] +epoch 1 done, use time: 0.22123003006, global metrics: LOSS=[2.344321] +batch: 2, LOSS: [2.0882485] +batch: 4, LOSS: [2.006743] +batch: 6, LOSS: [1.9231766] +batch: 8, LOSS: [1.8850241] +batch: 10, LOSS: [1.8829436] +batch: 12, LOSS: [1.9336565] +batch: 14, LOSS: [1.9784685] +epoch 2 done, use time: 0.212922096252, global metrics: LOSS=[1.9934461] +PaddleRec Finish +``` +## 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/match/dssm +2. 在data目录下载并解压数据集,命令如下: +``` +cd data +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz +tar xzf simnet_dataset-1.0.0.tar.gz +rm simnet_dataset-1.0.0.tar.gz +``` +3. 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,您在解压数据集后,可以看见目录中存在一个名为zhidao的文件。然后能可以在python3环境下运行我们提供的preprocess.py文件。即可生成可以直接用于训练的数据目录test.txt,train.txt和label.txt。将其放入train和test目录下以备训练时调用。命令如下: +``` +mv data/zhidao ./ +rm -rf data +python3 preprocess.py +rm -f ./train/train.txt +mv train.txt ./train +rm -f ./test/test.txt +mv test.txt test +cd .. +``` +经过预处理的格式: +训练集为三个稀疏的BOW方式的向量:query,pos,neg +测试集为两个稀疏的BOW方式的向量:query,pos +label.txt中对应的测试集中的标签 + +4. 退回dssm目录中,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) +将dataset_train中的batch_size从8改为128 +将文件model.py中的 hit_prob = fluid.layers.slice(prob, axes=[0, 1], starts=[0, 0], ends=[8, 1]) + 改为hit_prob = fluid.layers.slice(prob, axes=[0, 1], starts=[0, 0], ends=[128, 1]).当您需要改变batchsize的时候,end中第一个参数也需要随之变化 + +5. 执行脚本,开始训练.脚本会运行python -m paddlerec.run -m ./config.yaml启动训练,并将结果输出到result文件中。然后启动transform.py整合数据,最后计算出正逆序指标: +``` +sh run.sh +``` + +输出结果示例: +``` +................run................. +!!! The CPU_NUM is not specified, you should set CPU_NUM in the environment variable list. +CPU_NUM indicates that how many CPUPlace are used in the current task. +And if this parameter are set as N (equal to the number of physical CPU core) the program may be faster. + +export CPU_NUM=32 # for example, set CPU_NUM as number of physical CPU core which is 32. + +!!! The default number of CPU_NUM=1. +I0821 07:16:04.512531 32200 parallel_executor.cc:440] The Program will be executed on CPU using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. +I0821 07:16:04.515708 32200 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 +I0821 07:16:04.518872 32200 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True +I0821 07:16:04.520995 32200 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0 +75 +pnr: 2.25581395349 +query_num: 11 +pair_num: 184 184 +equal_num: 44 +正序率: 0.692857142857 +97 43 +``` +6. 提醒:因为采取较小的数据集进行训练和测试,得到指标的浮动程度会比较大。如果得到的指标不合预期,可以多次执行步骤5,即可获得合理的指标。 + +## 进阶使用 + +## FAQ diff --git a/models/match/dssm/run.sh b/models/match/dssm/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..5248d311a3d2b6365dff9ce2bd08fd75f2e599ee --- /dev/null +++ b/models/match/dssm/run.sh @@ -0,0 +1,24 @@ +# 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. +#!/bin/bash +echo "................run................." +python -m paddlerec.run -m ./config.yaml >result1.txt +grep -i "query_doc_sim" ./result1.txt >./result2.txt +sed '$d' result2.txt >result.txt +rm -f result1.txt +rm -f result2.txt +python transform.py +sort -t $'\t' -k1,1 -k 2nr,2 pair.txt >result.txt +rm -f pair.txt +python ../../../tools/cal_pos_neg.py result.txt diff --git a/models/match/dssm/transform.py b/models/match/dssm/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..3740ed7c094fc0ea84ff69fd1c10b7af79fb94a8 --- /dev/null +++ b/models/match/dssm/transform.py @@ -0,0 +1,54 @@ +# 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 random +import numpy as np +import sklearn.metrics + +label = [] +filename = './data/label.txt' +f = open(filename, "r") +f.readline() +num = 0 +for line in f.readlines(): + num = num + 1 + line = line.strip() + label.append(line) +f.close() +print(num) + +filename = './result.txt' +sim = [] +for line in open(filename): + line = line.strip().split(",") + line[1] = line[1].split(":") + line = line[1][1].strip(" ") + line = line.strip("[") + line = line.strip("]") + sim.append(float(line)) + +filename = './data/test/test.txt' +f = open(filename, "r") +f.readline() +query = [] +for line in f.readlines(): + line = line.strip().split("\t") + query.append(line[0]) +f.close() + +filename = 'pair.txt' +f = open(filename, "w") +for i in range(len(sim)): + f.write(str(query[i]) + "\t" + str(sim[i]) + "\t" + str(label[i]) + "\n") +f.close() diff --git a/models/match/match-pyramid/readme.md b/models/match/match-pyramid/readme.md index c0aaa483d1517cd8cb61e45dddb94716c7ec9639..2960d58e64b80bed8f4c5b0f6f2ff67ce14aed00 100644 --- a/models/match/match-pyramid/readme.md +++ b/models/match/match-pyramid/readme.md @@ -1,6 +1,42 @@ # match-pyramid文本匹配模型 -## 介绍 +以下是本例的简要目录结构及说明: + +``` +├── data #样例数据 + ├── process.py #数据处理脚本 + ├── relation.test.fold1.txt #评估计算指标时用到的关系文件 + ├── train + ├── train.txt #训练数据样例 + ├── test + ├── test.txt #测试数据样例 +├── __init__.py +├── README.md #文档 +├── model.py #模型文件 +├── config.yaml #配置文件 +├── data_process.sh #数据下载和处理脚本 +├── eval.py #计算指标的评估程序 +├── run.sh #一键运行程序 +├── test_reader.py #测试集读取程序 +├── train_reader.py #训练集读取程序 +``` + +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) + +## 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) +- [论文复现](#论文复现) +- [进阶使用](#进阶使用) +- [FAQ](#FAQ) + + +## 模型简介 在许多自然语言处理任务中,匹配两个文本是一个基本问题。一种有效的方法是从单词,短语和句子中提取有意义的匹配模式以产生匹配分数。受卷积神经网络在图像识别中的成功启发,神经元可以根据提取的基本视觉模式(例如定向的边角和边角)捕获许多复杂的模式,所以我们尝试将文本匹配建模为图像识别问题。本模型对齐原作者庞亮开源的tensorflow代码:https://github.com/pl8787/MatchPyramid-TensorFlow/blob/master/model/model_mp.py, 实现了下述论文中提出的Match-Pyramid模型: ```text @@ -19,8 +55,23 @@ 3.关系文件:关系文件被用来存储两个句子之间的关系,如query 和document之间的关系。例如:relation.train.fold1.txt, relation.test.fold1.txt 4.嵌入层文件:我们将预训练的词向量存储在嵌入文件中。例如:embed_wiki-pdc_d50_norm -## 数据下载和预处理 -本文提供了数据集的下载以及一键生成训练和测试数据的预处理脚本,您可以直接一键运行:bash data_process.sh +## 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/match/match-pyramid/config.yaml +``` + +## 论文复现 +1. 确认您当前所在目录为PaddleRec/models/match/match-pyramid +2. 本文提供了原数据集的下载以及一键生成训练和测试数据的预处理脚本,您可以直接一键运行:bash data_process.sh 执行该脚本,会从国内源的服务器上下载Letor07数据集,删除掉data文件夹中原有的relation.test.fold1.txt和relation.train.fold1.txt,并将完整的数据集解压到data文件夹。随后运行 process.py 将全量训练数据放置于`./data/train`,全量测试数据放置于`./data/test`。并生成用于初始化embedding层的embedding.npy文件 执行该脚本的理想输出为: ``` @@ -69,9 +120,11 @@ data/embed_wiki-pdc_d50_norm [./data/relation.test.fold1.txt] Instance size: 13652 ``` +3. 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) -## 一键训练并测试评估 -本文提供了一键执行训练,测试和评估的脚本,您可以直接一键运行:bash run.sh +4. 随后,您直接一键运行:bash run.sh 即可得到复现的论文效果 执行该脚本后,会执行python -m paddlerec.run -m ./config.yaml 命令开始训练并测试模型,将测试的结果保存到result.txt文件,最后通过执行eval.py进行评估得到数据的map指标 执行该脚本的理想输出为: ``` @@ -79,16 +132,7 @@ data/embed_wiki-pdc_d50_norm 13651 336 ('map=', 0.420878322843591) -``` - -## 每个文件的作用 -paddlerec可以: -通过config.yaml规定模型的参数 -通过model.py规定模型的组网 -使用train_reader.py读取训练集中的数据 -使用test_reader.py读取测试集中的数据。 -本文额外提供: -data_process.sh用来一键处理数据 -run.sh用来一键启动训练,直接得出测试结果 -eval.py通过保存的测试结果,计算map指标 -如需详细了解paddlerec的使用方法请参考https://github.com/PaddlePaddle/PaddleRec/blob/master/README_CN.md 页面下方的教程。 +``` +## 进阶使用 + +## FAQ diff --git a/models/match/match-pyramid/run.sh b/models/match/match-pyramid/run.sh index 3eccc10a990d563ed1dd5db2ad8ec3a73042ee69..9384899ee30b145f9d10ff66f9bdf6a18b1d8860 100644 --- a/models/match/match-pyramid/run.sh +++ b/models/match/match-pyramid/run.sh @@ -1,6 +1,6 @@ #!/bin/bash echo "................run................." python -m paddlerec.run -m ./config.yaml >result1.txt -grep -A1 "prediction" ./result1.txt >./result.txt +grep -i "prediction" ./result1.txt >./result.txt rm -f result1.txt python eval.py diff --git a/models/match/multiview-simnet/config.yaml b/models/match/multiview-simnet/config.yaml index d6e0af5b568280bf55480eae8d209cc3dd771903..9c418fe8430f0a204b4a44bec01bcfb3e29c1346 100755 --- a/models/match/multiview-simnet/config.yaml +++ b/models/match/multiview-simnet/config.yaml @@ -18,12 +18,12 @@ workspace: "models/match/multiview-simnet" # list of dataset dataset: - name: dataset_train # name of dataset to distinguish different datasets - batch_size: 2 + batch_size: 128 type: DataLoader # or QueueDataset data_path: "{workspace}/data/train" - sparse_slots: "1 2 3" + sparse_slots: "0 1 2" - name: dataset_infer # name - batch_size: 2 + batch_size: 1 type: DataLoader # or QueueDataset data_path: "{workspace}/data/test" sparse_slots: "1 2" @@ -34,17 +34,17 @@ hyper_parameters: class: Adam learning_rate: 0.0001 strategy: async - query_encoder: "bow" - title_encoder: "bow" + query_encoder: "gru" + title_encoder: "gru" query_encode_dim: 128 title_encode_dim: 128 - sparse_feature_dim: 1000001 + sparse_feature_dim: 1439 embedding_dim: 128 hidden_size: 128 margin: 0.1 # select runner by name -mode: train_runner +mode: [train_runner,infer_runner] # config of each runner. # runner is a kind of paddle training class, which wraps the train/infer process. runner: @@ -62,12 +62,14 @@ runner: save_inference_fetch_varnames: [] # fetch vars of save inference init_model_path: "" # load model path print_interval: 1 + phases: phase1 - name: infer_runner class: infer # device to run training or infer device: cpu print_interval: 1 - init_model_path: "increment/0" # load model path + init_model_path: "increment/1" # load model path + phases: phase2 # runner will run all the phase in each epoch phase: @@ -75,7 +77,7 @@ phase: model: "{workspace}/model.py" # user-defined model dataset_name: dataset_train # select dataset by name thread_num: 1 -#- name: phase2 -# model: "{workspace}/model.py" # user-defined model -# dataset_name: dataset_infer # select dataset by name -# thread_num: 1 +- name: phase2 + model: "{workspace}/model.py" # user-defined model + dataset_name: dataset_infer # select dataset by name + thread_num: 1 diff --git a/models/match/multiview-simnet/data/preprocess.py b/models/match/multiview-simnet/data/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..414281150661a016cb83a055ac82837a58ccdb32 --- /dev/null +++ b/models/match/multiview-simnet/data/preprocess.py @@ -0,0 +1,128 @@ +# 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. +#encoding=utf-8 + +import os +import sys +import numpy as np +import random + +f = open("./zhidao", "r") +lines = f.readlines() +f.close() + +#建立字典 +word_dict = {} +for line in lines: + line = line.strip().split("\t") + text = line[0].split(" ") + line[1].split(" ") + for word in text: + if word in word_dict: + continue + else: + word_dict[word] = len(word_dict) + 1 + +f = open("./zhidao", "r") +lines = f.readlines() +f.close() + +lines = [line.strip().split("\t") for line in lines] + +#建立以query为key,以负例为value的字典 +neg_dict = {} +for line in lines: + if line[2] == "0": + if line[0] in neg_dict: + neg_dict[line[0]].append(line[1]) + else: + neg_dict[line[0]] = [line[1]] + +#建立以query为key,以正例为value的字典 +pos_dict = {} +for line in lines: + if line[2] == "1": + if line[0] in pos_dict: + pos_dict[line[0]].append(line[1]) + else: + pos_dict[line[0]] = [line[1]] + +#划分训练集和测试集 +query_list = list(pos_dict.keys()) +#print(len(query_list)) +random.shuffle(query_list) +train_query = query_list[:90] +test_query = query_list[90:] + +#获得训练集 +train_set = [] +for query in train_query: + for pos in pos_dict[query]: + if query not in neg_dict: + continue + for neg in neg_dict[query]: + train_set.append([query, pos, neg]) +random.shuffle(train_set) + +#获得测试集 +test_set = [] +for query in test_query: + for pos in pos_dict[query]: + test_set.append([query, pos, 1]) + if query not in neg_dict: + continue + for neg in neg_dict[query]: + test_set.append([query, neg, 0]) +random.shuffle(test_set) + +#训练集中的query,pos,neg转化格式 +f = open("train.txt", "w") +for line in train_set: + query = line[0].strip().split(" ") + pos = line[1].strip().split(" ") + neg = line[2].strip().split(" ") + query_list = [] + for word in query: + query_list.append(word_dict[word]) + pos_list = [] + for word in pos: + pos_list.append(word_dict[word]) + neg_list = [] + for word in neg: + neg_list.append(word_dict[word]) + f.write(' '.join(["0:" + str(x) for x in query_list]) + " " + ' '.join([ + "1:" + str(x) for x in pos_list + ]) + " " + ' '.join(["2:" + str(x) for x in neg_list]) + "\n") +f.close() + +#测试集中的query和pos转化格式 +f = open("test.txt", "w") +fa = open("label.txt", "w") +fb = open("testquery.txt", "w") +for line in test_set: + query = line[0].strip().split(" ") + pos = line[1].strip().split(" ") + label = line[2] + query_list = [] + for word in query: + query_list.append(word_dict[word]) + pos_list = [] + for word in pos: + pos_list.append(word_dict[word]) + f.write(' '.join(["0:" + str(x) for x in query_list]) + " " + ' '.join( + ["1:" + str(x) for x in pos_list]) + "\n") + fa.write(str(label) + "\n") + fb.write(','.join([str(x) for x in query_list]) + "\n") +f.close() +fa.close() +fb.close() diff --git a/models/match/multiview-simnet/data/test/test.txt b/models/match/multiview-simnet/data/test/test.txt index cfb0675ff714459bc47d1e54bb1c707770238296..a9691d88873bf334ad9dddf125a115d3ccb692f4 100755 --- a/models/match/multiview-simnet/data/test/test.txt +++ b/models/match/multiview-simnet/data/test/test.txt @@ -1,10 +1,104 @@ -224289:0 126379:0 284519:0 549329:0 750666:0 393772:0 586898:0 736887:0 48785:0 906517:0 229162:1 483485:1 739835:1 29957:1 694497:1 997508:1 556876:1 717791:1 232176:1 430356:1 -366182:0 82062:0 708883:0 949128:0 798964:0 639103:0 409033:0 79301:0 405607:0 342616:0 61552:1 560547:1 3760:1 754734:1 98496:1 472427:1 979596:1 750283:1 492028:1 801383:1 -969571:0 405187:0 756217:0 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2:124 2:298 2:48 +0:155 0:1 0:2 0:91 0:156 0:157 0:158 1:14 1:578 1:1 1:2 1:91 1:156 1:157 1:1430 2:159 2:91 2:156 2:157 2:158 +0:117 0:118 0:4 0:119 0:120 0:121 0:122 0:48 1:1 1:123 1:117 1:118 1:4 1:130 1:13 1:454 1:86 1:298 1:28 1:117 1:196 1:48 1:22 2:119 2:132 2:118 2:208 2:48 +0:75 0:68 0:30 0:432 0:86 0:433 0:434 0:178 0:4 0:435 1:75 1:68 1:30 1:432 1:144 1:365 1:436 1:178 1:433 1:435 2:181 2:602 2:432 2:144 2:433 2:434 2:178 2:4 2:435 +0:329 0:330 0:75 0:331 0:332 0:333 0:332 0:334 0:332 0:335 0:172 0:67 0:68 0:69 1:329 1:330 1:75 1:331 1:332 1:333 1:332 1:334 1:332 1:335 1:172 1:67 1:68 1:69 2:159 2:336 2:337 2:67 2:331 2:332 2:333 2:332 2:26 2:334 2:332 2:4 diff --git a/models/match/multiview-simnet/evaluate_reader.py b/models/match/multiview-simnet/evaluate_reader.py old mode 100755 new mode 100644 index 7d800fe426435ba609aca1c5b62123fb13c306ab..b3e7849fe4bc9a8766e4bab72f50e266d949dfc9 --- a/models/match/multiview-simnet/evaluate_reader.py +++ b/models/match/multiview-simnet/evaluate_reader.py @@ -1,5 +1,4 @@ # 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 @@ -40,8 +39,8 @@ class Reader(ReaderBase): padding = 0 output = [(slot, []) for slot in self.all_slots] for elem in elements: - feasign, slot = elem.split(':') - if not self._all_slots_dict.has_key(slot): + slot, feasign = elem.split(':') + if slot not in self._all_slots_dict: continue self._all_slots_dict[slot][0] = True index = self._all_slots_dict[slot][1] diff --git a/models/match/multiview-simnet/generate_synthetic_data.py b/models/match/multiview-simnet/generate_synthetic_data.py deleted file mode 100755 index eb60e5c82f9decc2cfcd87da7bc6832ca98ee9d4..0000000000000000000000000000000000000000 --- a/models/match/multiview-simnet/generate_synthetic_data.py +++ /dev/null @@ -1,102 +0,0 @@ -# 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 random - - -class Dataset: - def __init__(self): - pass - - -class SyntheticDataset(Dataset): - def __init__(self, - sparse_feature_dim, - query_slot_num, - title_slot_num, - dataset_size=10000): - # ids are randomly generated - self.ids_per_slot = 10 - self.sparse_feature_dim = sparse_feature_dim - self.query_slot_num = query_slot_num - self.title_slot_num = title_slot_num - self.dataset_size = dataset_size - - def _reader_creator(self, is_train): - def generate_ids(num, space): - return [random.randint(0, space - 1) for i in range(num)] - - def reader(): - for i in range(self.dataset_size): - query_slots = [] - pos_title_slots = [] - neg_title_slots = [] - for i in range(self.query_slot_num): - qslot = generate_ids(self.ids_per_slot, - self.sparse_feature_dim) - qslot = [str(fea) + ':' + str(i) for fea in qslot] - query_slots += qslot - for i in range(self.title_slot_num): - pt_slot = generate_ids(self.ids_per_slot, - self.sparse_feature_dim) - pt_slot = [ - str(fea) + ':' + str(i + self.query_slot_num) - for fea in pt_slot - ] - pos_title_slots += pt_slot - if is_train: - for i in range(self.title_slot_num): - nt_slot = generate_ids(self.ids_per_slot, - self.sparse_feature_dim) - nt_slot = [ - str(fea) + ':' + - str(i + self.query_slot_num + self.title_slot_num) - for fea in nt_slot - ] - neg_title_slots += nt_slot - yield query_slots + pos_title_slots + neg_title_slots - else: - yield query_slots + pos_title_slots - - return reader - - def train(self): - return self._reader_creator(True) - - def valid(self): - return self._reader_creator(True) - - def test(self): - return self._reader_creator(False) - - -if __name__ == '__main__': - sparse_feature_dim = 1000001 - query_slots = 1 - title_slots = 1 - dataset_size = 10 - dataset = SyntheticDataset(sparse_feature_dim, query_slots, title_slots, - dataset_size) - train_reader = dataset.train() - test_reader = dataset.test() - - with open("data/train/train.txt", 'w') as fout: - for data in train_reader(): - fout.write(' '.join(data)) - fout.write("\n") - - with open("data/test/test.txt", 'w') as fout: - for data in test_reader(): - fout.write(' '.join(data)) - fout.write("\n") diff --git a/models/match/multiview-simnet/reader.py b/models/match/multiview-simnet/reader.py old mode 100755 new mode 100644 index 8fe6402876f814a8b6692285c9855796d1d05b6e..d3cb170f44127f6444405ea98f179c21691d678f --- a/models/match/multiview-simnet/reader.py +++ b/models/match/multiview-simnet/reader.py @@ -43,8 +43,8 @@ class Reader(ReaderBase): padding = 0 output = [(slot, []) for slot in self.all_slots] for elem in elements: - feasign, slot = elem.split(':') - if not self._all_slots_dict.has_key(slot): + slot, feasign = elem.split(':') + if slot not in self._all_slots_dict: continue self._all_slots_dict[slot][0] = True index = self._all_slots_dict[slot][1] diff --git a/models/match/multiview-simnet/readme.md b/models/match/multiview-simnet/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..4a59d851bbc26365d9120985d8163bd105f9ec95 --- /dev/null +++ b/models/match/multiview-simnet/readme.md @@ -0,0 +1,127 @@ +# multiview-simnet文本匹配模型 + +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 +├── config.yaml #配置文件 +├── run.sh #运行脚本,在效果复现时使用 +├── transform.py #整理格式准备计算指标的程序 +├── reader.py #读者需要自定义数据集时供读者参考 +├── evaluate_reader.py #读者需要自定义数据集时供读者参考 +``` +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 +在个性化推荐场景中,推荐系统给用户提供的项目(Item)列表通常是通过个性化的匹配模型计算出来的。在现实世界中,一个用户可能有很多个视角的特征,比如用户Id,年龄,项目的点击历史等。一个项目,举例来说,新闻资讯,也会有多种视角的特征比如新闻标题,新闻类别等。多视角Simnet模型是可以融合用户以及推荐项目的多个视角的特征并进行个性化匹配学习的一体化模型。 多视角Simnet模型包括多个编码器模块,每个编码器被用在不同的特征视角上。当前,项目中提供Bag-of-Embedding编码器,Temporal-Convolutional编码器,和Gated-Recurrent-Unit编码器。我们会逐渐加入稀疏特征场景下比较实用的编码器到这个项目中。模型的训练方法,当前采用的是Pairwise ranking模式进行训练,即针对一对具有关联的User-Item组合,随机实用一个Item作为负例进行排序学习。 + +模型的具体细节可以阅读论文[MultiView-Simnet](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf): +
+ +
+ +## 数据准备 +我们公开了自建的测试集,包括百度知道、ECOM、QQSIM、UNICOM 四个数据集。这里我们选取百度知道数据集来进行训练。执行以下命令可以获取上述数据集。 +``` +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz +tar xzf simnet_dataset-1.0.0.tar.gz +rm simnet_dataset-1.0.0.tar.gz +``` + +数据格式为一个标识句子的slot,后跟一个句子中词的token。两者形成{slot:token}的形式标识一个词: +``` +0:358 0:206 0:205 0:250 0:9 0:3 0:207 0:10 0:330 0:164 1:1144 1:217 1:206 1:9 1:3 1:207 1:10 1:398 1:2 2:217 2:206 2:9 2:3 2:207 2:10 2:398 2:2 +0:358 0:206 0:205 0:250 0:9 0:3 0:207 0:10 0:330 0:164 1:951 1:952 1:206 1:9 1:3 1:207 1:10 1:398 2:217 2:206 2:9 2:3 2:207 2:10 2:398 2:2 +``` + +## 运行环境 +PaddlePaddle>=1.7.2 +python 2.7 +PaddleRec >=0.1 +os : linux + +## 快速开始 +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/match/multiview-simnet/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/match/multiview-simnet +2. 在data目录下载并解压数据集,命令如下: +``` +cd data +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz +tar xzf simnet_dataset-1.0.0.tar.gz +rm -f simnet_dataset-1.0.0.tar.gz +mv data/zhidao ./ +rm -rf data +``` +3. 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,您在解压数据集后,可以看见目录中存在一个名为zhidao的文件。然后能可以在python3环境下运行我们提供的preprocess.py文件。即可生成可以直接用于训练的数据目录test.txt,train.txt,label.txt和testquery.txt。将其放入train和test目录下以备训练时调用。命令如下: +``` +python3 preprocess.py +rm -f ./train/train.txt +mv train.txt ./train +rm -f ./test/test.txt +mv test.txt ./test +cd .. +``` +4. 退回tagspace目录中,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) + +5. 执行脚本,开始训练.脚本会运行python -m paddlerec.run -m ./config.yaml启动训练,并将结果输出到result文件中。然后启动格式整理程序transform,最后计算正逆序比: +``` +sh run.sh +``` + +运行结果大致如下: +``` +................run................. +!!! The CPU_NUM is not specified, you should set CPU_NUM in the environment variable list. +CPU_NUM indicates that how many CPUPlace are used in the current task. +And if this parameter are set as N (equal to the number of physical CPU core) the program may be faster. + +export CPU_NUM=32 # for example, set CPU_NUM as number of physical CPU core which is 32. + +!!! The default number of CPU_NUM=1. +I0821 14:24:57.255358 7888 parallel_executor.cc:440] The Program will be executed on CPU using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. +I0821 14:24:57.259166 7888 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 +I0821 14:24:57.262634 7888 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True +I0821 14:24:57.264791 7888 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0 +103 +pnr: 1.17674418605 +query_num: 11 +pair_num: 468 468 +equal_num: 0 +正序率: 0.540598290598 +253 215 +``` +6. 提醒:因为采取较小的数据集进行训练和测试,得到指标的浮动程度会比较大。如果得到的指标不合预期,可以多次执行步骤5,即可获得合理的指标。 +## 进阶使用 + +## FAQ diff --git a/models/match/multiview-simnet/data_process.sh b/models/match/multiview-simnet/run.sh old mode 100755 new mode 100644 similarity index 62% rename from models/match/multiview-simnet/data_process.sh rename to models/match/multiview-simnet/run.sh index c8633cc7a41f62a29eee1778251b72a6f3b601eb..3d9a5bdbba832653ce30b556c19dd8449cda8869 --- a/models/match/multiview-simnet/data_process.sh +++ b/models/match/multiview-simnet/run.sh @@ -1,5 +1,3 @@ -#! /bin/bash - # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -14,11 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. - -set -e -echo "begin to prepare data" - -mkdir -p data/train -mkdir -p data/test - -python generate_synthetic_data.py +#!/bin/bash +echo "................run................." +python -m paddlerec.run -m ./config.yaml >result1.txt +grep -i "query_pt_sim" ./result1.txt >./result2.txt +sed '$d' result2.txt >result.txt +rm -f result1.txt +rm -f result2.txt +python transform.py +sort -t $'\t' -k1,1 -k 2nr,2 pair.txt >result.txt +rm -f pair.txt +python ../../../tools/cal_pos_neg.py result.txt diff --git a/models/match/multiview-simnet/transform.py b/models/match/multiview-simnet/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..2f26b05715a87e768476673bf6531270c2d400e6 --- /dev/null +++ b/models/match/multiview-simnet/transform.py @@ -0,0 +1,53 @@ +# 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 random +import numpy as np + +label = [] +filename = './data/label.txt' +f = open(filename, "r") +f.readline() +num = 0 +for line in f.readlines(): + num = num + 1 + line = line.strip() + label.append(line) +f.close() +print(num) + +filename = './result.txt' +sim = [] +for line in open(filename): + line = line.strip().split(",") + line[1] = line[1].split(":") + line = line[1][1].strip(" ") + line = line.strip("[") + line = line.strip("]") + sim.append(float(line)) + +filename = './data/testquery.txt' +f = open(filename, "r") +f.readline() +query = [] +for line in f.readlines(): + line = line.strip() + query.append(line) +f.close() + +filename = 'pair.txt' +f = open(filename, "w") +for i in range(len(sim)): + f.write(str(query[i]) + "\t" + str(sim[i]) + "\t" + str(label[i]) + "\n") +f.close() diff --git a/models/match/readme.md b/models/match/readme.md index 5ca8cac2cbb9be3662cdd80cdbad5cf7abe8b6cd..440ad9796605aefe94c2f822daebb73062bfed24 100755 --- a/models/match/readme.md +++ b/models/match/readme.md @@ -1,7 +1,7 @@ # 匹配模型库 ## 简介 -我们提供了常见的匹配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。实现的模型包括 [DSSM](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/dssm)、[MultiView-Simnet](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/multiview-simnet)。 +我们提供了常见的匹配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。实现的模型包括 [DSSM](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/dssm)、[MultiView-Simnet](http://gitlab.baidu.com/tangwei12/paddlerec/tree/develop/models/match/multiview-simnet)、[match-pyramid](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/match/match-pyramid)。 模型算法库在持续添加中,欢迎关注。 @@ -18,6 +18,8 @@ | :------------------: | :--------------------: | :---------: | | DSSM | Deep Structured Semantic Models | [CIKM 2013][Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf) | | MultiView-Simnet | Multi-view Simnet for Personalized recommendation | [WWW 2015][A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf) | +| match-pyramid | Text Matching as Image Recognition | [arXiv W2016][Text Matching as Image Recognition](https://arxiv.org/pdf/1602.06359.pdf) | + 下面是每个模型的简介(注:图片引用自链接中的论文) @@ -31,24 +33,26 @@
-## 使用教程(快速开始) -### 训练 -```shell -git clone https://github.com/PaddlePaddle/PaddleRec.git paddle-rec -cd paddle-rec +[match-pyramid](https://arxiv.org/pdf/1602.06359.pdf): +
+ +
+## 使用教程(快速开始) +### 训练&预测 +每个模型都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/match/dssm/config.yaml # dssm python -m paddlerec.run -m models/match/multiview-simnet/config.yaml # multiview-simnet +python -m paddlerec.run -m models/contentunderstanding/match-pyramid/config.yaml #match-pyramid ``` +### 效果复现 +每个模型下的readme中都有详细的效果复现的教程,您可以进入模型的目录中详细查看 -### 预测 -```shell -# 修改对应模型的config.yaml, workspace配置为当前目录的绝对路径 -# 修改对应模型的config.yaml,mode配置infer_runner -# 示例: mode: train_runner -> mode: infer_runner -# infer_runner中 class配置为 class: infer -# 修改phase阶段为infer的配置,参照config注释 +### 模型效果 (测试) -# 修改完config.yaml后 执行: -python -m paddlerec.run -m ./config.yaml # 以dssm为例 -``` +| 数据集 | 模型 | 正逆序比 | map | +| :------------------: | :--------------------: | :---------: |:---------: | +| zhidao | DSSM | 2.25 | -- | +| Letor07 | match-pyramid | -- | 0.42 | +| zhidao | multiview-simnet | 1.72 | -- | diff --git a/models/multitask/mmoe/README.md b/models/multitask/mmoe/README.md index 19f4674d97e56630acea2029396d12668e7dc3b1..d652e48dffac97481e75d827ec35854bae141dae 100644 --- a/models/multitask/mmoe/README.md +++ b/models/multitask/mmoe/README.md @@ -1,149 +1,123 @@ -# MMOE - - 以下是本例的简要目录结构及说明: - -``` -├── data # 文档 - ├── train #训练数据 - ├── train_data.txt - ├── test #测试数据 - ├── test_data.txt - ├── run.sh - ├── data_preparation.py -├── __init__.py -├── config.yaml #配置文件 -├── census_reader.py #数据读取文件 -├── model.py #模型文件 -``` - -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](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#模型简介) -- [数据准备](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#数据准备) -- [运行环境](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#运行环境) -- [快速开始](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#快速开始) -- [论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#论文复现) -- [进阶使用](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#进阶使用) -- [FAQ](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#FAQ) - -## 模型简介 - -多任务模型通过学习不同任务的联系和差异,可提高每个任务的学习效率和质量。多任务学习的的框架广泛采用shared-bottom的结构,不同任务间共用底部的隐层。这种结构本质上可以减少过拟合的风险,但是效果上可能受到任务差异和数据分布带来的影响。 论文[《Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts》]( https://www.kdd.org/kdd2018/accepted-papers/view/modeling-task-relationships-in-multi-task-learning-with-multi-gate-mixture- )中提出了一个Multi-gate Mixture-of-Experts(MMOE)的多任务学习结构。MMOE模型刻画了任务相关性,基于共享表示来学习特定任务的函数,避免了明显增加参数的缺点。 - -我们在Paddlepaddle定义MMOE的网络结构,在开源数据集Census-income Data上验证模型效果,两个任务的auc分别为: - -1.income - -> max_mmoe_test_auc_income:0.94937 -> -> mean_mmoe_test_auc_income:0.94465 - -2.marital - -> max_mmoe_test_auc_marital:0.99419 -> -> mean_mmoe_test_auc_marital:0.99324 - -若进行精度验证,请参考[论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#论文复现)部分。 - -本项目支持功能 +# MMOE + + 以下是本例的简要目录结构及说明: + +``` +├── data # 文档 + ├── train #训练数据 + ├── train_data.txt + ├── test #测试数据 + ├── test_data.txt + ├── run.sh + ├── data_preparation.py +├── __init__.py +├── config.yaml #配置文件 +├── census_reader.py #数据读取文件 +├── model.py #模型文件 +``` + +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](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#模型简介) +- [数据准备](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#数据准备) +- [运行环境](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#运行环境) +- [快速开始](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#快速开始) +- [论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#论文复现) +- [进阶使用](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#进阶使用) +- [FAQ](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#FAQ) + +## 模型简介 + +多任务模型通过学习不同任务的联系和差异,可提高每个任务的学习效率和质量。多任务学习的的框架广泛采用shared-bottom的结构,不同任务间共用底部的隐层。这种结构本质上可以减少过拟合的风险,但是效果上可能受到任务差异和数据分布带来的影响。 论文[《Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts》]( https://www.kdd.org/kdd2018/accepted-papers/view/modeling-task-relationships-in-multi-task-learning-with-multi-gate-mixture- )中提出了一个Multi-gate Mixture-of-Experts(MMOE)的多任务学习结构。MMOE模型刻画了任务相关性,基于共享表示来学习特定任务的函数,避免了明显增加参数的缺点。 + +我们在Paddlepaddle定义MMOE的网络结构,在开源数据集Census-income Data上验证模型效果。 + +若进行精度验证,请参考[论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/mmoe#论文复现)部分。 + +本项目支持功能 训练:单机CPU、单机单卡GPU、单机多卡GPU、本地模拟参数服务器训练、增量训练,配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md) 预测:单机CPU、单机单卡GPU ;配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md) ## 数据准备 -数据地址: [Census-income Data](https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census.tar.gz ) +数据地址: [Census-income Data](https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census.tar.gz ) + + +生成的格式以逗号为分割点 + +``` +0,0,73,0,0,0,0,1700.09,0,0 +``` + +完整的大数据参考论文复现部分。 -数据解压后, 在run.sh脚本文件中添加文件的路径,并运行脚本。 +## 运行环境 -```sh -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/ +PaddlePaddle>=1.7.2 -python data_preparation.py --train_path ${train_path} \ - --test_path ${test_path} \ - --train_data_path ${train_data_path}\ - --test_data_path ${test_data_path} +python 2.7/3.5/3.6/3.7 +PaddleRec >=0.1 + +os : windows/linux/macos + +## 快速开始 + +### 单机训练 + +CPU环境 + +在config.yaml文件中设置好设备,epochs等。 + +``` +dataset: +- name: dataset_train + batch_size: 5 + type: QueueDataset + data_path: "{workspace}/data/train" + data_converter: "{workspace}/census_reader.py" +- name: dataset_infer + batch_size: 5 + type: QueueDataset + data_path: "{workspace}/data/train" + data_converter: "{workspace}/census_reader.py" ``` -生成的格式以逗号为分割点 +### 单机预测 +CPU环境 + +在config.yaml文件中设置好epochs、device等参数。 ``` -0,0,73,0,0,0,0,1700.09,0,0 +- name: infer_runner + class: infer + init_model_path: "increment/0" + device: cpu ``` +## 论文复现 + +数据下载,我们提供了在百度云上预处理好的数据,可以直接训练 + +``` +wget https://paddlerec.bj.bcebos.com/mmoe/train_data.csv +wget https://paddlerec.bj.bcebos.com/mmoe/test_data.csv +wget https://paddlerec.bj.bcebos.com/mmoe/config_all.yaml +``` + +用原论文的完整数据复现论文效果需要在config.yaml中修改batch_size=32 gpu配置等,可参考config_all.yaml -## 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 - -### 单机训练 - -CPU环境 - -在config.yaml文件中设置好设备,epochs等。 - -``` -dataset: -- name: dataset_train - batch_size: 5 - type: QueueDataset - data_path: "{workspace}/data/train" - data_converter: "{workspace}/census_reader.py" -- name: dataset_infer - batch_size: 5 - type: QueueDataset - data_path: "{workspace}/data/train" - data_converter: "{workspace}/census_reader.py" -``` - -### 单机预测 - -CPU环境 - -在config.yaml文件中设置好epochs、device等参数。 - -``` -- name: infer_runner - class: infer - init_model_path: "increment/0" - device: cpu -``` - -## 论文复现 - -用原论文的完整数据复现论文效果需要在config.yaml中修改batch_size=1000, thread_num=8, epoch_num=4 - 使用gpu p100 单卡训练 6.5h 测试auc: best:0.9940, mean:0.9932 - -修改后运行方案:修改config.yaml中的'workspace'为config.yaml的目录位置,执行 - -``` -python -m paddlerec.run -m /home/your/dir/config.yaml #调试模式 直接指定本地config的绝对路径 -``` - -## 进阶使用 - + +``` +python -m paddlerec.run -m /home/your/dir/config_all.yaml #调试模式 直接指定本地config的绝对路径 +``` + +## 进阶使用 + ## FAQ diff --git a/models/multitask/mmoe/config.yaml b/models/multitask/mmoe/config.yaml index ee2447ffeef1ab1228cf2a00bef456ed1acac396..0c79f9e6086c70b9c0d4b9cc26fc8bdfe76662fe 100644 --- a/models/multitask/mmoe/config.yaml +++ b/models/multitask/mmoe/config.yaml @@ -16,13 +16,13 @@ workspace: "models/multitask/mmoe" dataset: - name: dataset_train - batch_size: 1 - type: DataLoader # or QueueDataset + batch_size: 5 + type: QueueDataset data_path: "{workspace}/data/train" data_converter: "{workspace}/census_reader.py" - name: dataset_infer - batch_size: 1 - type: DataLoader # or QueueDataset + batch_size: 5 + type: QueueDataset data_path: "{workspace}/data/train" data_converter: "{workspace}/census_reader.py" @@ -44,7 +44,7 @@ runner: class: train device: cpu epochs: 3 - save_checkpoint_interval: 2 + save_checkpoint_interval: 1 save_inference_interval: 4 save_checkpoint_path: "increment" save_inference_path: "inference" diff --git a/models/rank/dnn/config.yaml b/models/rank/dnn/config.yaml index e984501548529bcd0e65bbf6e53554a3b340aa47..aa84a5070470cba750f7832644a9ce676c1d4ddd 100755 --- a/models/rank/dnn/config.yaml +++ b/models/rank/dnn/config.yaml @@ -67,7 +67,6 @@ runner: save_inference_path: "inference" # save inference path save_inference_feed_varnames: [] # feed vars of save inference save_inference_fetch_varnames: [] # fetch vars of save inference - init_model_path: "" # load model path print_interval: 10 phases: [phase1] diff --git a/models/recall/word2vec/README.md b/models/recall/word2vec/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7f6ed7522801822b9281df84a6a6924f30eb2f87 --- /dev/null +++ b/models/recall/word2vec/README.md @@ -0,0 +1,248 @@ +# Skip-Gram W2V + +以下是本例的简要目录结构及说明: + +``` +├── data #样例数据 + ├── train + ├── convert_sample.txt + ├── test + ├── sample.txt + ├── dict + ├── word_count_dict.txt + ├── word_id_dict.txt +├── preprocess.py # 数据预处理文件 +├── __init__.py +├── README.md # 文档 +├── model.py #模型文件 +├── config.yaml #配置文件 +├── data_prepare.sh #一键数据处理脚本 +├── w2v_reader.py #训练数据reader +├── w2v_evaluate_reader.py # 预测数据reader +├── infer.py # 自定义预测脚本 +├── utils.py # 自定义预测中用到的reader等工具 +``` + +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) +- [论文复现](#论文复现) +- [进阶使用](#进阶使用) +- [FAQ](#FAQ) + +## 模型简介 +本例实现了skip-gram模式的word2vector模型,如下图所示: +
+ +
+以每一个词为中心词X,然后在窗口内和临近的词Y组成样本对(X,Y)用于网络训练。在实际训练过程中还会根据自定义的负采样率生成负样本来加强训练的效果 +具体的训练思路如下: +
+ +
+
+推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/124377)教程获取更详细的信息。
+
+本模型配置默认使用demo数据集,若进行精度验证,请参考[论文复现](#论文复现)部分。
+
+本项目支持功能
+
+训练:单机CPU、本地模拟参数服务器训练、增量训练,配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md)
+
+预测:单机CPU;配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md)
+
+## 数据处理
+为和样例数据路径区分,全量训练数据、测试数据、词表文件会依次保存在data/all_train, data/all_test, data/all_dict文件夹中。
+```
+mkdir -p data/all_dict
+mkdir -p data/all_train
+mkdir -p data/all_test
+```
+本示例中全量数据处理共包含三步:
+- Step1: 数据下载。
+ ```
+ # 全量训练集
+ mkdir raw_data
+ wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
+ tar xvf 1-billion-word-language-modeling-benchmark-r13output.tar
+ mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ raw_data/
+
+ # 测试集
+ wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
+ tar xzvf test_dir.tar -C raw_data
+ mv raw_data/data/test_dir/* data/all_test/
+ ```
+
+- Step2: 训练据预处理。包含三步,第一步,根据英文语料生成词典,中文语料可以通过修改text_strip方法自定义处理方法。
+ ```
+ python preprocess.py --build_dict --build_dict_corpus_dir raw_data/training-monolingual.tokenized.shuffled --dict_path raw_data/word_count_dict.txt
+ ```
+ 得到的词典格式为词<空格>词频,低频词用'UNK'表示,如下所示:
+ ```
+ the 1061396
+ of 593677
+ and 416629
+ one 411764
+ in 372201
+ a 325873
+