diff --git a/models/rank/deep_crossing/__init__.py b/models/rank/deep_crossing/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54 --- /dev/null +++ b/models/rank/deep_crossing/__init__.py @@ -0,0 +1,13 @@ +# 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. diff --git a/models/rank/deep_crossing/config.yaml b/models/rank/deep_crossing/config.yaml new file mode 100755 index 0000000000000000000000000000000000000000..bd2c3acf8757622f864d7fb01e17232f87297157 --- /dev/null +++ b/models/rank/deep_crossing/config.yaml @@ -0,0 +1,75 @@ +# 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. + + +# global settings +debug: false +workspace: "paddlerec.models.rank.deep_crossing" + +dataset: + - name: train_sample + type: QueueDataset + batch_size: 5 + data_path: "{workspace}/../dataset/Criteo_data/sample_data/train" + sparse_slots: "label feat_idx" + dense_slots: "feat_value:39" + - name: infer_sample + type: QueueDataset + batch_size: 5 + data_path: "{workspace}/../dataset/Criteo_data/sample_data/train" + sparse_slots: "label feat_idx" + dense_slots: "feat_value:39" + +hyper_parameters: + # 用户自定义配置 + optimizer: + class: SGD + learning_rate: 0.0001 + sparse_feature_number: 1086460 + sparse_feature_dim: 8 + reg: 0.001 + num_field: 39 + residual_unit_num: 4 + residual_w_dim: 128 + +mode: train_runner +# if infer, change mode to "infer_runner" and change phase to "infer_phase" + +runner: + - name: train_runner + trainer_class: single_train + epochs: 1 + device: cpu + init_model_path: "" + save_checkpoint_interval: 1 + save_inference_interval: 1 + save_checkpoint_path: "increment" + save_inference_path: "inference" + print_interval: 1 + - name: infer_runner + trainer_class: single_infer + epochs: 1 + device: cpu + init_model_path: "increment/0" + print_interval: 1 + +phase: +- name: phase1 + model: "{workspace}/model.py" + dataset_name: train_sample + thread_num: 1 +#- name: infer_phase +# model: "{workspace}/model.py" +# dataset_name: infer_sample +# thread_num: 1 diff --git a/models/rank/deep_crossing/model.py b/models/rank/deep_crossing/model.py new file mode 100755 index 0000000000000000000000000000000000000000..ee587454f439cddabc57d74f2cd42a09a7346445 --- /dev/null +++ b/models/rank/deep_crossing/model.py @@ -0,0 +1,144 @@ +# 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 math +from collections import OrderedDict + +import paddle.fluid as fluid + +from paddlerec.core.utils import envs +from paddlerec.core.model import Model as ModelBase + + +class Model(ModelBase): + def __init__(self, config): + ModelBase.__init__(self, config) + + def _init_hyper_parameters(self): + self.is_distributed = True if envs.get_trainer( + ) == "CtrTrainer" else False + self.sparse_feature_number = envs.get_global_env( + "hyper_parameters.sparse_feature_number", None) + self.sparse_feature_dim = envs.get_global_env( + "hyper_parameters.sparse_feature_dim", None) + self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4) + self.num_field = envs.get_global_env("hyper_parameters.num_field", + None) + self.residual_unit_num = envs.get_global_env( + "hyper_parameters.residual_unit_num", 1) + self.residual_w_dim = envs.get_global_env( + "hyper_parameters.residual_w_dim", 32) + self.concat_size = self.num_field * (self.sparse_feature_dim + 1) + + def resudual_unit(self, x): + inter_layer = fluid.layers.fc( + input=x, + size=self.residual_w_dim, + act='relu', + param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( + scale=1.0 / math.sqrt(self.concat_size)))) + output = fluid.layers.fc( + input=inter_layer, + size=self.concat_size, + act=None, + param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( + scale=1.0 / math.sqrt(self.residual_w_dim)))) + output = output + x + return fluid.layers.relu6(output, threshold=10000000.0) + + def net(self, inputs, is_infer=False): + raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1 + raw_feat_value = self._dense_data_var[0] # batch_size * num_field + self.label = self._sparse_data_var[0] # batch_size * 1 + + init_value_ = 0.1 + + feat_idx = raw_feat_idx + feat_value = fluid.layers.reshape( + raw_feat_value, + [-1, self.num_field, 1]) # batch_size * num_field * 1 + + # ------------------------- first order term -------------------------- + + first_weights_re = fluid.embedding( + input=feat_idx, + is_sparse=True, + is_distributed=self.is_distributed, + dtype='float32', + size=[self.sparse_feature_number + 1, 1], + padding_idx=0, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.TruncatedNormalInitializer( + loc=0.0, scale=init_value_), + regularizer=fluid.regularizer.L1DecayRegularizer(self.reg)) + ) # (batch_size * num_field) * 1 * 1(embedding_size) + first_weights = fluid.layers.reshape( + first_weights_re, + shape=[-1, self.num_field, 1]) # batch_size * num_field * 1 + + # ------------------------- second order term -------------------------- + + feat_embeddings_re = fluid.embedding( + input=feat_idx, + is_sparse=True, + is_distributed=self.is_distributed, + dtype='float32', + size=[self.sparse_feature_number + 1, self.sparse_feature_dim], + padding_idx=0, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.TruncatedNormalInitializer( + loc=0.0, + scale=init_value_ / + math.sqrt(float(self.sparse_feature_dim)))) + ) # (batch_size * num_field) * 1 * embedding_size + feat_embeddings = fluid.layers.reshape( + feat_embeddings_re, + shape=[-1, self.num_field, self.sparse_feature_dim + ]) # batch_size * num_field * embedding_size + feat_embeddings = feat_embeddings * feat_value # batch_size * num_field * embedding_size + + concated = fluid.layers.concat( + [feat_embeddings, first_weights], axis=2) + concated = fluid.layers.reshape( + concated, + shape=[-1, self.num_field * (self.sparse_feature_dim + 1)]) + + for _ in range(self.residual_unit_num): + concated = self.resudual_unit(concated) + + predict = fluid.layers.fc( + input=concated, + size=1, + act="sigmoid", + param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( + scale=1 / math.sqrt(self.concat_size)))) + + self.predict = predict + + cost = fluid.layers.log_loss( + input=self.predict, label=fluid.layers.cast(self.label, + "float32")) # log_loss + avg_cost = fluid.layers.reduce_sum(cost) + + self._cost = avg_cost + + predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1) + label_int = fluid.layers.cast(self.label, 'int64') + auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d, + label=label_int, + slide_steps=0) + self._metrics["AUC"] = auc_var + self._metrics["BATCH_AUC"] = batch_auc_var + if is_infer: + self._infer_results["AUC"] = auc_var