diff --git a/LICENSE b/LICENSE
index 4720bde..5b0a09e 100644
--- a/LICENSE
+++ b/LICENSE
@@ -218,7 +218,6 @@
For layers.ScaledDotProductAttention:
Copyright (C) 2018 pengshuang@Github
-
-----------------------------------------------------------------------
For models.DCNv2.CrossNetMix:
@@ -235,3 +234,19 @@
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.
+ -----------------------------------------------------------------------
+
+ For model_zoo.GDCN:
+ Copyright (C) 2023 sdilbaz@github
+
+ 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/README.md b/README.md
index dec29a9..6a7fc43 100644
--- a/README.md
+++ b/README.md
@@ -72,23 +72,24 @@ Click-through rate (CTR) prediction is a critical task for many industrial appli
| 35 | KDD'21 | [AOANet](./model_zoo/AOANet) | [Architecture and Operation Adaptive Network for Online Recommendations](https://dl.acm.org/doi/10.1145/3447548.3467133) :triangular_flag_on_post:**Didi Chuxing** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/AOANet) | `torch` |
| 36 | AAAI'23 | [FinalMLP](./model_zoo/FinalMLP) | [FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction](https://arxiv.org/abs/2304.00902) :triangular_flag_on_post:**Huawei** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/FinalMLP) | `torch` |
| 37 | SIGIR'23 | [FINAL](./model_zoo/FINAL) | FINAL: Factorized Interaction Layer for CTR Prediction :triangular_flag_on_post:**Huawei** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/FINAL) | `torch` |
+| 38 | CIKM'23 | [GDCN](./model_zoo/GDCN) | [Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/3583780.3615089) :triangular_flag_on_post:**Microsoft** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/FinalMLP) | `torch` |
|| :open_file_folder: **Behavior Sequence Modeling** |
|---|
|
-| 38 | KDD'18 | [DIN](./model_zoo/DIN) | [Deep Interest Network for Click-Through Rate Prediction](https://www.kdd.org/kdd2018/accepted-papers/view/deep-interest-network-for-click-through-rate-prediction) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIN) | `torch` |
-| 39 | AAAI'19 | [DIEN](./model_zoo/DIEN) | [Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/abs/1809.03672) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIEN) | `torch` |
-| 40 | DLP-KDD'19 | [BST](./model_zoo/BST) | [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/abs/1905.06874) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/BST) | `torch` |
-| 41 | CIKM'20 | [DMIN](./model_zoo/DMIN) | [Deep Multi-Interest Network for Click-through Rate Prediction](https://dl.acm.org/doi/10.1145/3340531.3412092) :triangular_flag_on_post:**Alibaba** | | `torch` |
-| 42 | AAAI'20 | [DMR](./model_zoo/DMR) | [Deep Match to Rank Model for Personalized Click-Through Rate Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/5346) :triangular_flag_on_post:**Alibaba** | | `torch` |
-| 43 | Arxiv'21 | [ETA](./model_zoo/ETA) | [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://arxiv.org/abs/2108.04468) :triangular_flag_on_post:**Alibaba** | | `torch` |
-| 44 | CIKM'22 | [SDIM](./model_zoo/SDIM) | [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://arxiv.org/abs/2205.10249) :triangular_flag_on_post:**Meituan** | | `torch` |
+| 39 | KDD'18 | [DIN](./model_zoo/DIN) | [Deep Interest Network for Click-Through Rate Prediction](https://www.kdd.org/kdd2018/accepted-papers/view/deep-interest-network-for-click-through-rate-prediction) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIN) | `torch` |
+| 40 | AAAI'19 | [DIEN](./model_zoo/DIEN) | [Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/abs/1809.03672) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/DIEN) | `torch` |
+| 41 | DLP-KDD'19 | [BST](./model_zoo/BST) | [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/abs/1905.06874) :triangular_flag_on_post:**Alibaba** | [:arrow_upper_right:](https://github.com/openbenchmark/BARS/tree/main/ctr_prediction/benchmarks/BST) | `torch` |
+| 42 | CIKM'20 | [DMIN](./model_zoo/DMIN) | [Deep Multi-Interest Network for Click-through Rate Prediction](https://dl.acm.org/doi/10.1145/3340531.3412092) :triangular_flag_on_post:**Alibaba** | | `torch` |
+| 43 | AAAI'20 | [DMR](./model_zoo/DMR) | [Deep Match to Rank Model for Personalized Click-Through Rate Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/5346) :triangular_flag_on_post:**Alibaba** | | `torch` |
+| 44 | Arxiv'21 | [ETA](./model_zoo/ETA) | [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://arxiv.org/abs/2108.04468) :triangular_flag_on_post:**Alibaba** | | `torch` |
+| 45 | CIKM'22 | [SDIM](./model_zoo/SDIM) | [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://arxiv.org/abs/2205.10249) :triangular_flag_on_post:**Meituan** | | `torch` |
|| :open_file_folder: **Dynamic Weight Network** |
|---|
|
-| 45 | NeurIPS'22 | [APG](./model_zoo/APG) | [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://arxiv.org/abs/2203.16218) :triangular_flag_on_post:**Alibaba** | | `torch` |
-| 46 | Arxiv'23 | [PPNet](./model_zoo/PEPNet) | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |
+| 46 | NeurIPS'22 | [APG](./model_zoo/APG) | [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://arxiv.org/abs/2203.16218) :triangular_flag_on_post:**Alibaba** | | `torch` |
+| 47 | Arxiv'23 | [PPNet](./model_zoo/PEPNet) | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |
|| :open_file_folder: **Multi-Task Modeling** |
|---|
|
-| 47 | MachineLearn'97 | [SharedBottom](./model_zoo/multitask/SharedBottom) | [Multitask Learning](https://link.springer.com/article/10.1023/A:1007379606734) | | `torch` |
-| 48 | KDD'18 | [MMoE](./model_zoo/multitask/MMOE) | [Modeling Task Relationships in Multi-task Learning with Multi-Gate Mixture-of-Experts](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007) :triangular_flag_on_post:**Google** | | `torch` |
-| 49 | KDD'18 | [PLE](./model_zoo/multitask/PLE) | [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/10.1145/3383313.3412236) :triangular_flag_on_post:**Tencent** | | `torch` |
+| 48 | MachineLearn'97 | [SharedBottom](./model_zoo/multitask/SharedBottom) | [Multitask Learning](https://link.springer.com/article/10.1023/A:1007379606734) | | `torch` |
+| 49 | KDD'18 | [MMoE](./model_zoo/multitask/MMOE) | [Modeling Task Relationships in Multi-task Learning with Multi-Gate Mixture-of-Experts](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007) :triangular_flag_on_post:**Google** | | `torch` |
+| 50 | KDD'18 | [PLE](./model_zoo/multitask/PLE) | [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/10.1145/3383313.3412236) :triangular_flag_on_post:**Tencent** | | `torch` |
|| :open_file_folder: **Multi-Domain Modeling** |
|---|
|
-| 50 | Arxiv'23 | PEPNet | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |
+| 51 | Arxiv'23 | PEPNet | [PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information](https://arxiv.org/abs/2302.01115) :triangular_flag_on_post:**KuaiShou** | | `torch` |
+ :point_right: See [reusable dataset splits for CTR prediction](https://openbenchmark.github.io/BARS/datasets/README.html).
diff --git a/model_zoo/GDCN/README.md b/model_zoo/GDCN/README.md
new file mode 100644
index 0000000..b28adac
--- /dev/null
+++ b/model_zoo/GDCN/README.md
@@ -0,0 +1,93 @@
+## GDCN
+
+| [Overview](#Overview) | [Configuration](#Configuration) | [Implementation](#Implementation) | [Discussion](#Discussion) |
+| :--: | :--: | :--: | :--: |
+
+### Overview
+
+GDCN is a CTR prediction model that learns explicit and bounded-degree cross features. The model is published in the following paper:
+
++ [Towards Deeper, Lighter and Interpretable Cross Network for
+CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/3583780.3615089), in CIKM 2023.
+
+**Key components:**
+
++ *CrossNet*: The component provides explicit feature crossing with bounded degree.
+
+ $$x_{l+1} = x_0x_l^Tw + b + x_l$$
+
++ *Dynamic embedding size*: It provides a formula to compute the embedding size of each feature field.
+
+ $$emb\_dim = 6\times(vocab\_size)^{1/4}$$
+
+### Configuration
+
+The `model_config.yaml` file contains all the model hyper-parameters as follows.
+
+| Params | Type | Default | Description |
+| ---------------------- | --------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| model | str | "DCN" | model name, which should be same with model class name |
+| dataset_id | str | "TBD" | dataset_id to be determined |
+| loss | str | "binary_crossentropy" | loss function |
+| metrics | list | ['logloss', 'AUC'] | a list of metrics for evaluation |
+| task | str | "binary_classification" | task type supported: ```"regression"```, ```"binary_classification"``` |
+| optimizer | str | "adam" | optimizer used for training |
+| learning_rate | float | 1.0e-3 | learning rate |
+| embedding_regularizer | float/str | 0 | regularization weight for embedding matrix: L2 regularization is applied by default. Other optional examples: ```"l2(1.e-3)"```, ```"l1(1.e-3)"```, ```"l1_l2(1.e-3, 1.e-3)"```. |
+| net_regularizer | float/str | 0 | regularization weight for network parameters: L2 regularization is applied by default. Other optional examples: ```"l2(1.e-3)"```, ```"l1(1.e-3)"```, ```"l1_l2(1.e-3, 1.e-3)"```. |
+| batch_size | int | 10000 | batch size, usually a large number for CTR prediction task |
+| embedding_dim | int | 32 | embedding dimension of features. Note that field-wise embedding_dim can be specified in ```feature_specs```. |
+| dnn_hidden_units | list | [1024, 512, 256] | hidden units in DNN |
+| dnn_activations | str/list | "relu" | activation function in DNN. Particularly, layer-wise activations can be specified as a list, e.g., ["relu", "leakyrelu", "sigmoid"] |
+| num_cross_layers | int | 3 | number of cross layers in CrossNet |
+| net_dropout | float | 0 | dropout rate in DNN |
+| batch_norm | bool | False | whether using BN in DNN |
+| epochs | int | 100 | the max number of epochs for training, which can early stop via monitor metrics. |
+| shuffle | bool | True | whether shuffle the data samples for each epoch of training |
+| seed | int | 20222023 | the random seed used for reproducibility |
+| monitor | str/dict | {'AUC': 1, 'logloss': -1} | the monitor metrics for early stopping. It supports a single metric, e.g., ```"AUC"```. It also supports multiple metrics using a dict, e.g., {"AUC": 2, "logloss": -1} means ```2*AUC - logloss```. |
+| monitor_mode | str | 'max' | ```"max"``` means that the higher the better, while ```"min"``` denotes that the lower the better. |
+| model_root | str | './checkpoints/' | the dir to save model checkpoints and running logs |
+| num_workers | int | 3 | the number of workers for data loader |
+| verbose | int | 1 | 0 for salience while 1 for verbose logging with tqdm |
+| early_stop_patience | int | 2 | training is stopped when monitor metric fails to become better for ```early_stop_patience=2```consective evaluation intervals. |
+| pickle_feature_encoder | bool | True | whether to pickle the feature encoder during preprocessing. It is used when input ```data_format="csv"```. |
+| save_best_only | bool | True | whether to save the best model checkpoint only |
+| eval_steps | int/None | None | evaluate the model on validation data every ```eval_steps```. By default, ```None``` means evaluation every epoch. |
+| debug_mode | bool | False | used for code testing. When setting it to ```True```, the ```experiment_id``` will be randomly generated to avoid interleaving when running multiple processes for parameter tunning by ```run_param_tuner.py```. |
+| group_id | None (optional) | None | required for metrics like ```gAUC```, ```NDCG```. |
+| use_features | None (optional) | None | used for feature selection, i.e., only selecting an ordered subset of features as model input |
+| feature_specs | dict (optional) | None | used for specifying field-wise configurations, such as ```embedding_dim```, ```feature_encoder``` for a specific field. |
+
+
+### Implementation
+
+**Code structure:**
+
+```
+├── config # 配置文件夹
+│ ├── dataset_config.yaml # 数据集配置文件
+│ └── model_config.yaml # 模型配置文件
+├── src # 模型代码文件夹
+│ └── GDCN.py # 模型代码
+├── fuxictr_version.py # fuxictr加载及版本检查文件
+├── README.md # 使用说明
+├── requirements.txt # 依赖文件
+└── run_expid.py # 执行脚本文件
+```
+
+**Requirements:**
+
+The model is tested with the following dependencies.
+
++ fuxictr==2.0.0
+
++ pytorch==1.11
+
+**Get started:**
+
+Running the model on the tiny data:
+
+```
+python run_expid.py --expid GDCNP_test --gpu 0
+```
diff --git a/model_zoo/GDCN/config/dataset_config.yaml b/model_zoo/GDCN/config/dataset_config.yaml
new file mode 100644
index 0000000..c45414b
--- /dev/null
+++ b/model_zoo/GDCN/config/dataset_config.yaml
@@ -0,0 +1,8 @@
+### Tiny data for tests only
+tiny_h5:
+ data_root: ../../data/
+ data_format: h5
+ train_data: ../../data/tiny_h5/train.h5
+ valid_data: ../../data/tiny_h5/valid.h5
+ test_data: ../../data/tiny_h5/test.h5
+
diff --git a/model_zoo/GDCN/config/model_config.yaml b/model_zoo/GDCN/config/model_config.yaml
new file mode 100644
index 0000000..f2cc6ef
--- /dev/null
+++ b/model_zoo/GDCN/config/model_config.yaml
@@ -0,0 +1,83 @@
+Base:
+ model_root: './checkpoints/'
+ num_workers: 3
+ verbose: 1
+ early_stop_patience: 2
+ pickle_feature_encoder: True
+ save_best_only: True
+ eval_steps: null
+ debug_mode: False
+ group_id: null
+ use_features: null
+ feature_specs: null
+ feature_config: null
+
+GDCNP_test:
+ model: GDCNP
+ dataset_id: tiny_h5
+ loss: 'binary_crossentropy'
+ metrics: ['logloss', 'AUC']
+ task: binary_classification
+ optimizer: adam
+ learning_rate: 1.0e-3
+ embedding_regularizer: 1.e-8
+ net_regularizer: 0
+ batch_size: 128
+ embedding_dim: 4
+ dnn_hidden_units: [64, 32]
+ dnn_activations: relu
+ crossing_layers: 3
+ net_dropout: 0
+ batch_norm: False
+ epochs: 1
+ shuffle: True
+ seed: 2019
+ monitor: 'AUC'
+ monitor_mode: 'max'
+
+GDCNS_test:
+ model: GDCNS
+ dataset_id: tiny_h5
+ loss: 'binary_crossentropy'
+ metrics: ['logloss', 'AUC']
+ task: binary_classification
+ optimizer: adam
+ learning_rate: 1.0e-3
+ embedding_regularizer: 1.e-8
+ net_regularizer: 0
+ batch_size: 128
+ embedding_dim: 4
+ dnn_hidden_units: [64, 32]
+ dnn_activations: relu
+ crossing_layers: 3
+ net_dropout: 0
+ batch_norm: False
+ epochs: 1
+ shuffle: True
+ seed: 2019
+ monitor: 'AUC'
+ monitor_mode: 'max'
+
+GDCN_default: # This is a config template
+ model: GDCNP
+ dataset_id: TBD
+ loss: 'binary_crossentropy'
+ metrics: ['logloss', 'AUC']
+ task: binary_classification
+ optimizer: adam
+ learning_rate: 1.0e-3
+ embedding_regularizer: 0
+ net_regularizer: 0
+ batch_size: 10000
+ embedding_dim: 32
+ dnn_hidden_units: [1024, 512, 256]
+ dnn_activations: relu
+ num_cross_layers: 3
+ net_dropout: 0
+ batch_norm: False
+ epochs: 100
+ shuffle: True
+ seed: 20222023
+ monitor: {'AUC': 1, 'logloss': -1}
+ monitor_mode: 'max'
+
diff --git a/model_zoo/GDCN/fuxictr_version.py b/model_zoo/GDCN/fuxictr_version.py
new file mode 100644
index 0000000..666e5f0
--- /dev/null
+++ b/model_zoo/GDCN/fuxictr_version.py
@@ -0,0 +1,3 @@
+# pip install -U fuxictr
+import fuxictr
+assert fuxictr.__version__ >= "2.0.0"
diff --git a/model_zoo/GDCN/run_expid.py b/model_zoo/GDCN/run_expid.py
new file mode 100644
index 0000000..2d44f3b
--- /dev/null
+++ b/model_zoo/GDCN/run_expid.py
@@ -0,0 +1,88 @@
+# =========================================================================
+# Copyright (C) 2022. Huawei Technologies Co., Ltd. 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 os
+os.chdir(os.path.dirname(os.path.realpath(__file__)))
+import sys
+import logging
+import fuxictr_version
+from fuxictr import datasets
+from datetime import datetime
+from fuxictr.utils import load_config, set_logger, print_to_json, print_to_list
+from fuxictr.features import FeatureMap
+from fuxictr.pytorch.torch_utils import seed_everything
+from fuxictr.pytorch.dataloaders import H5DataLoader
+from fuxictr.preprocess import FeatureProcessor, build_dataset
+import src as model_zoo
+import gc
+import argparse
+import os
+from pathlib import Path
+
+
+if __name__ == '__main__':
+ ''' Usage: python run_expid.py --config {config_dir} --expid {experiment_id} --gpu {gpu_device_id}
+ '''
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--config', type=str, default='./config/', help='The config directory.')
+ parser.add_argument('--expid', type=str, default='DeepFM_test', help='The experiment id to run.')
+ parser.add_argument('--gpu', type=int, default=-1, help='The gpu index, -1 for cpu')
+ args = vars(parser.parse_args())
+
+ experiment_id = args['expid']
+ params = load_config(args['config'], experiment_id)
+ params['gpu'] = args['gpu']
+ set_logger(params)
+ logging.info("Params: " + print_to_json(params))
+ seed_everything(seed=params['seed'])
+
+ data_dir = os.path.join(params['data_root'], params['dataset_id'])
+ feature_map_json = os.path.join(data_dir, "feature_map.json")
+ if params["data_format"] == "csv":
+ # Build feature_map and transform h5 data
+ feature_encoder = FeatureProcessor(**params)
+ params["train_data"], params["valid_data"], params["test_data"] = \
+ build_dataset(feature_encoder, **params)
+ feature_map = FeatureMap(params['dataset_id'], data_dir)
+ feature_map.load(feature_map_json, params)
+ logging.info("Feature specs: " + print_to_json(feature_map.features))
+
+ model_class = getattr(model_zoo, params['model'])
+ model = model_class(feature_map, **params)
+ model.count_parameters() # print number of parameters used in model
+
+ train_gen, valid_gen = H5DataLoader(feature_map, stage='train', **params).make_iterator()
+ model.fit(train_gen, validation_data=valid_gen, **params)
+
+ logging.info('****** Validation evaluation ******')
+ valid_result = model.evaluate(valid_gen)
+ del train_gen, valid_gen
+ gc.collect()
+
+ logging.info('******** Test evaluation ********')
+ test_gen = H5DataLoader(feature_map, stage='test', **params).make_iterator()
+ test_result = {}
+ if test_gen:
+ test_result = model.evaluate(test_gen)
+
+ result_filename = Path(args['config']).name.replace(".yaml", "") + '.csv'
+ with open(result_filename, 'a+') as fw:
+ fw.write(' {},[command] python {},[exp_id] {},[dataset_id] {},[train] {},[val] {},[test] {}\n' \
+ .format(datetime.now().strftime('%Y%m%d-%H%M%S'),
+ ' '.join(sys.argv), experiment_id, params['dataset_id'],
+ "N.A.", print_to_list(valid_result), print_to_list(test_result)))
+
diff --git a/model_zoo/GDCN/src/GDCN.py b/model_zoo/GDCN/src/GDCN.py
new file mode 100644
index 0000000..d953c9f
--- /dev/null
+++ b/model_zoo/GDCN/src/GDCN.py
@@ -0,0 +1,145 @@
+# =========================================================================
+# Copyright (C) 2023 sdilbaz@github
+#
+# 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 torch
+from torch import nn
+from fuxictr.pytorch.models import BaseModel
+from fuxictr.pytorch.layers import FeatureEmbedding, MLP_Block
+
+
+class GDCNP(BaseModel):
+ def __init__(self,
+ feature_map,
+ model_id="GDCNP",
+ gpu=-1,
+ learning_rate=1e-3,
+ embedding_dim=10,
+ dnn_hidden_units=[],
+ dnn_activations="ReLU",
+ num_cross_layers=3,
+ net_dropout=0,
+ batch_norm=False,
+ embedding_regularizer=None,
+ net_regularizer=None,
+ **kwargs):
+ super(GDCNP, self).__init__(feature_map,
+ model_id=model_id,
+ gpu=gpu,
+ embedding_regularizer=embedding_regularizer,
+ net_regularizer=net_regularizer,
+ **kwargs)
+ self.embedding_layer = FeatureEmbedding(feature_map, embedding_dim)
+ input_dim = feature_map.sum_emb_out_dim()
+ self.dnn = MLP_Block(input_dim=input_dim,
+ output_dim=None, # output hidden layer
+ hidden_units=dnn_hidden_units,
+ hidden_activations=dnn_activations,
+ output_activation=None,
+ dropout_rates=net_dropout,
+ batch_norm=batch_norm) \
+ if dnn_hidden_units else None # in case of only crossing net used
+ self.cross_net = GateCorssLayer(input_dim, num_cross_layers)
+ self.fc = torch.nn.Linear(dnn_hidden_units[-1] + input_dim, 1)
+
+ self.compile(kwargs["optimizer"], kwargs["loss"], learning_rate)
+ self.reset_parameters()
+ self.model_to_device()
+
+ def forward(self, inputs):
+ X = self.get_inputs(inputs)
+ feature_emb = self.embedding_layer(X, flatten_emb=True)
+ cross_cn = self.cross_net(feature_emb)
+ cross_mlp = self.dnn(feature_emb)
+ y_pred = self.fc(torch.cat([cross_cn, cross_mlp], dim=1))
+ y_pred = self.output_activation(y_pred)
+ return_dict = {"y_pred": y_pred}
+ return return_dict
+
+class GDCNS(BaseModel):
+ def __init__(self,
+ feature_map,
+ model_id="GDCNS",
+ gpu=-1,
+ learning_rate=1e-3,
+ embedding_dim=10,
+ dnn_hidden_units=[],
+ dnn_activations="ReLU",
+ num_cross_layers=3,
+ net_dropout=0,
+ batch_norm=False,
+ embedding_regularizer=None,
+ net_regularizer=None,
+ **kwargs):
+ super(GDCNS, self).__init__(feature_map,
+ model_id=model_id,
+ gpu=gpu,
+ embedding_regularizer=embedding_regularizer,
+ net_regularizer=net_regularizer,
+ **kwargs)
+ self.embedding_layer = FeatureEmbedding(feature_map, embedding_dim)
+ input_dim = feature_map.sum_emb_out_dim()
+ self.dnn = MLP_Block(input_dim=input_dim,
+ output_dim=1, # output hidden layer
+ hidden_units=dnn_hidden_units,
+ hidden_activations=dnn_activations,
+ output_activation=None,
+ dropout_rates=net_dropout,
+ batch_norm=batch_norm) \
+ if dnn_hidden_units else None # in case of only crossing net used
+ self.cross_net = GateCorssLayer(input_dim, num_cross_layers)
+
+ self.compile(kwargs["optimizer"], kwargs["loss"], learning_rate)
+ self.reset_parameters()
+ self.model_to_device()
+
+ def forward(self, inputs):
+ X = self.get_inputs(inputs)
+ feature_emb = self.embedding_layer(X, flatten_emb=True)
+ cross_cn = self.cross_net(feature_emb)
+ y_pred = self.dnn(cross_cn)
+ y_pred = self.output_activation(y_pred)
+ return_dict = {"y_pred": y_pred}
+ return return_dict
+
+class GateCorssLayer(nn.Module):
+ # The core structure： gated corss layer.
+ def __init__(self, input_dim, cn_layers=3):
+ super().__init__()
+
+ self.cn_layers = cn_layers
+
+ self.w = nn.ModuleList([
+ nn.Linear(input_dim, input_dim, bias=False) for _ in range(cn_layers)
+ ])
+ self.wg = nn.ModuleList([
+ nn.Linear(input_dim, input_dim, bias=False) for _ in range(cn_layers)
+ ])
+
+ self.b = nn.ParameterList([nn.Parameter(
+ torch.zeros((input_dim,))) for _ in range(cn_layers)])
+
+ for i in range(cn_layers):
+ nn.init.uniform_(self.b[i].data)
+
+ self.activation = nn.Sigmoid()
+
+ def forward(self, x):
+ x0 = x
+ for i in range(self.cn_layers):
+ xw = self.w[i](x) # Feature Crossing
+ xg = self.activation(self.wg[i](x)) # Information Gate
+ x = x0 * (xw + self.b[i]) * xg + x
+ return x
diff --git a/model_zoo/GDCN/src/__init__.py b/model_zoo/GDCN/src/__init__.py
new file mode 100644
index 0000000..37caeaa
--- /dev/null
+++ b/model_zoo/GDCN/src/__init__.py
@@ -0,0 +1,4 @@
+from .GDCN import GDCNP, GDCNS
+
+
+