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.fnn"

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: 9
    reg: 0.001
    num_field: 39
    fc_sizes: [512, 256, 128, 32]

mode: train_FM_runner #for FM phase: train_FM_runner for dnn phase: train_DNN_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"

runner:
  - name: train_FM_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: train_DNN_runner
    trainer_class: single_train
    epochs: 1
    device: cpu
    init_model_path: "increment/0"
    load_vars: "embedding_1.w_0, embedding_0.w_0"
    save_checkpoint_interval: 1
    save_inference_interval: 1
    save_checkpoint_path: "increment_fnn"
    save_inference_path: "inference_fnn"
    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}/fm_model.py" # for FM phase: fm_model.py for dnn phase model.py
  dataset_name: train_sample
  thread_num: 1
#- name: infer_phase
#  model: "{workspace}/model.py"
#  dataset_name: infer_sample
#  thread_num: 1