dygraph_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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
import math
import numpy as np

import net
import sys


class DygraphModel():
    # def __init__(self):
    #     self.bucket = 100000
    #     self.absolute_limt = 200.0
    #
    # def rescale(self, number):
    #     if number > self.absolute_limt:
    #         number = self.absolute_limt
    #     elif number < -self.absolute_limt:
    #         number = -self.absolute_limt
    #     return (number + self.absolute_limt) / (self.absolute_limt * 2 + 1e-8)

    # define model
    def create_model(self, config):
        article_content_size = config.get(
            "hyper_parameters.article_content_size")
        article_title_size = config.get("hyper_parameters.article_title_size")
        browse_size = config.get("hyper_parameters.browse_size")
        neg_condidate_sample_size = config.get(
            "hyper_parameters.neg_condidate_sample_size")
        word_dimension = config.get("hyper_parameters.word_dimension")
        category_size = config.get("hyper_parameters.category_size")
        sub_category_size = config.get("hyper_parameters.sub_category_size")
        cate_dimension = config.get("hyper_parameters.category_dimension")
        word_dict_size = config.get("hyper_parameters.word_dict_size")
        return net.NAMLLayer(article_content_size, article_title_size,
                             browse_size, neg_condidate_sample_size,
                             word_dimension, category_size, sub_category_size,
                             cate_dimension, word_dict_size)

    # define feeds which convert numpy of batch data to paddle.tensor
    def create_feeds(self, batch, config):
        label = batch[0]
        return label, batch[1:]

    # define loss function by predicts and label
    def create_loss(self, raw_pred, label):
        cost = paddle.nn.functional.cross_entropy(
            input=raw_pred,
            label=paddle.cast(label, "float32"),
            soft_label=True)
        avg_cost = paddle.mean(x=cost)
        return avg_cost

    # define optimizer
    def create_optimizer(self, dy_model, config):
        lr = config.get("hyper_parameters.optimizer.learning_rate", 0.001)
        optimizer = paddle.optimizer.Adam(
            learning_rate=lr, parameters=dy_model.parameters())
        return optimizer

    # define metrics such as auc/acc
    # multi-task need to define multi metric
    def create_metrics(self):
        # metrics_list_name = ["acc"]
        # auc_metric = paddle.metric.Accuracy()
        metrics_list_name = ["auc"]
        auc_metric = paddle.metric.Auc()
        metrics_list = [auc_metric]
        return metrics_list, metrics_list_name

    # construct train forward phase
    def train_forward(self, dy_model, metrics_list, batch_data, config):
        labels, sparse_tensor = self.create_feeds(batch_data, config)

        raw = dy_model.forward(sparse_tensor)

        loss = paddle.nn.functional.cross_entropy(
            input=raw, label=paddle.cast(labels, "float32"), soft_label=True)

        soft_predict = paddle.nn.functional.sigmoid(
            paddle.reshape(raw, [-1, 1]))
        predict_2d = paddle.concat(x=[1 - soft_predict, soft_predict], axis=-1)
        labels = paddle.reshape(labels, [-1, 1])
        metrics_list[0].update(preds=predict_2d.numpy(), labels=labels.numpy())

        loss = paddle.mean(loss)
        print_dict = None
        return loss, metrics_list, print_dict

    def infer_forward(self, dy_model, metrics_list, batch_data, config):
        labels, sparse_tensor = self.create_feeds(batch_data, config)
        raw = dy_model.forward(sparse_tensor)
        #predict_raw = paddle.nn.functional.softmax(raw)

        soft_predict = paddle.nn.functional.sigmoid(
            paddle.reshape(raw, [-1, 1]))
        predict_2d = paddle.concat(x=[1 - soft_predict, soft_predict], axis=-1)
        labels = paddle.reshape(labels, [-1, 1])
        metrics_list[0].update(preds=predict_2d.numpy(), labels=labels.numpy())
        return metrics_list, None