tmp add fgcnn for bak

models/rank/fgcnn/__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/fgcnn/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.fgcnn"
+
+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: Adam
+        learning_rate: 0.0001
+    sparse_feature_number: 1086460
+    sparse_feature_dim: 9
+    is_sparse: False
+    use_batchnorm: False
+    filters: [38,40,42,44]
+    new_filters: [3,3,3,3]
+    pooling_size: [2,2,2,2]
+    use_dropout: False
+    dropout_prob: 0.9
+    fc_sizes: [400, 400, 400]
+    loss_type: "log_loss" # log_loss or square_loss
+    reg: 0.001
+    num_field: 39
+    act: "relu"
+
+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/fgcnn/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.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
+                                             False)
+        self.use_batchnorm = envs.get_global_env(
+            "hyper_parameters.use_batchnorm", False)
+        self.filters = envs.get_global_env("hyper_parameters.filters",
+                                           [38, 40, 42, 44])
+        self.filter_size = envs.get_global_env("hyper_parameters.filter_size",
+                                               [1, 9])
+        self.pooling_size = envs.get_global_env(
+            "hyper_parameters.pooling_size", [2, 2, 2, 2])
+        self.new_filters = envs.get_global_env("hyper_parameters.new_filters",
+                                               [3, 3, 3, 3])
+
+        self.use_dropout = envs.get_global_env("hyper_parameters.use_dropout",
+                                               False)
+        self.dropout_prob = envs.get_global_env(
+            "hyper_parameters.dropout_prob", None)
+        self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes",
+                                               None)
+        self.loss_type = envs.get_global_env("hyper_parameters.loss_type",
+                                             'logloss')
+        self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
+        self.num_field = envs.get_global_env("hyper_parameters.num_field",
+                                             None)
+        self.act = envs.get_global_env("hyper_parameters.act", None)
+
+    def net(self, inputs, is_infer=False):
+        raw_feat_idx = self._sparse_data_var[1]  # (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
+
+        # ------------------------- Embedding layers --------------------------
+
+        feat_embeddings_re = fluid.embedding(
+            input=feat_idx,
+            is_sparse=self.is_sparse,
+            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
+        featuer_generation_input = fluid.layers.reshape(
+            feat_embeddings,
+            shape=[0, 1, self.num_field, self.sparse_feature_dim])
+        new_feature_list = []
+        new_feature_field_num = 0
+        for i in range(len(self.filters)):
+            conv_out = fluid.layers.conv2d(
+                featuer_generation_input,
+                num_filters=self.filters[i],
+                filter_size=self.filter_size,
+                padding="SAME",
+                act="tanh")
+            pool_out = fluid.layers.pool2d(
+                conv_out,
+                pool_size=[self.pooling_size[i], 1],
+                pool_type="max",
+                pool_stride=[self.pooling_size[i], 1])
+            pool_out_shape = pool_out.shape[2]
+            new_feature_field_num += self.new_filters[i] * pool_out_shape
+            print("SHAPE>> {}".format(pool_out_shape))
+            flat_pool_out = fluid.layers.flatten(pool_out)
+            recombination_out = fluid.layers.fc(input=flat_pool_out,
+                                                size=self.new_filters[i] *
+                                                self.sparse_feature_dim *
+                                                pool_out_shape,
+                                                act='tanh')
+            new_feature_list.append(recombination_out)
+            featuer_generation_input = pool_out
+        new_featues = fluid.layers.concat(new_feature_list, axis=1)
+        new_features_map = fluid.layers.reshape(
+            new_featues,
+            shape=[0, new_feature_field_num, self.sparse_feature_dim])
+        print("new_feature shape: {}".format(new_features_map.shape))
+        #fluid.layers.Print(new_features_map)
+        all_features = fluid.layers.concat(
+            [feat_embeddings, new_features_map], axis=1)
+        #fluid.layers.Print(all_features)
+        print("all_feature shape: {}".format(all_features.shape))
+        interaction_list = []
+        fluid.layers.Print(all_features[:, 0, :])
+        for i in range(all_features.shape[1]):
+            for j in range(i + 1, all_features.shape[1]):
+
+                interaction_list.append(
+                    fluid.layers.reduce_sum(
+                        all_features[:, i, :] * all_features[:, j, :],
+                        dim=1,
+                        keep_dim=True))
+
+        # sum_square part
+        summed_features_emb = fluid.layers.reduce_sum(
+            feat_embeddings, 1)  # batch_size * embedding_size
+        summed_features_emb_square = fluid.layers.square(
+            summed_features_emb)  # batch_size * embedding_size
+
+        # square_sum part
+        squared_features_emb = fluid.layers.square(
+            feat_embeddings)  # batch_size * num_field * embedding_size
+        squared_sum_features_emb = fluid.layers.reduce_sum(
+            squared_features_emb, 1)  # batch_size * embedding_size
+
+        y_FM = 0.5 * (summed_features_emb_square - squared_sum_features_emb
+                      )  # batch_size * embedding_size
+
+        if self.use_batchnorm:
+            y_FM = fluid.layers.batch_norm(input=y_FM, is_test=is_infer)
+        if self.use_dropout:
+            y_FM = fluid.layers.dropout(
+                x=y_FM, dropout_prob=self.dropout_prob, is_test=is_infer)
+
+        # ------------------------- DNN --------------------------
+
+        y_dnn = y_FM
+        for s in self.layer_sizes:
+            if self.use_batchnorm:
+                y_dnn = fluid.layers.fc(
+                    input=y_dnn,
+                    size=s,
+                    act=self.act,
+                    param_attr=fluid.ParamAttr(initializer=fluid.initializer.
+                                               TruncatedNormalInitializer(
+                                                   loc=0.0,
+                                                   scale=init_value_ /
+                                                   math.sqrt(float(10)))),
+                    bias_attr=fluid.ParamAttr(initializer=fluid.initializer.
+                                              TruncatedNormalInitializer(
+                                                  loc=0.0, scale=init_value_)))
+                y_dnn = fluid.layers.batch_norm(
+                    input=y_dnn, act=self.act, is_test=is_infer)
+            else:
+                y_dnn = fluid.layers.fc(
+                    input=y_dnn,
+                    size=s,
+                    act=self.act,
+                    param_attr=fluid.ParamAttr(initializer=fluid.initializer.
+                                               TruncatedNormalInitializer(
+                                                   loc=0.0,
+                                                   scale=init_value_ /
+                                                   math.sqrt(float(10)))),
+                    bias_attr=fluid.ParamAttr(initializer=fluid.initializer.
+                                              TruncatedNormalInitializer(
+                                                  loc=0.0, scale=init_value_)))
+            if self.use_dropout:
+                y_dnn = fluid.layers.dropout(
+                    x=y_dnn, dropout_prob=self.dropout_prob, is_test=is_infer)
+        y_dnn = fluid.layers.fc(
+            input=y_dnn,
+            size=1,
+            act=None,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0, scale=init_value_)),
+            bias_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0, scale=init_value_)))
+
+        # ------------------------- Predict --------------------------
+
+        self.predict = fluid.layers.sigmoid(y_dnn)
+        if self.loss_type == "squqre_loss":
+            cost = fluid.layers.mse_loss(
+                input=self.predict,
+                label=fluid.layers.cast(self.label, "float32"))
+        else:
+            cost = fluid.layers.log_loss(
+                input=self.predict,
+                label=fluid.layers.cast(self.label,
+                                        "float32"))  # default 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