add deep_crossing

models/rank/deep_crossing/__init__.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

models/rank/deep_crossing/config.yaml

+# 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

models/rank/deep_crossing/model.py

+#   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