单机试验个性化推荐报错 TypeError: 'generator' object is not callable

[xlhlhlx]

自己定义了一个data reader，然后跑train的时候报错，但是单独运行data reader是可以正常生成样本的，具体报错的log如下：

自己写的data reader如下：

#!/usr/bin/python
#encoding=utf8
import sys
import os
import random

class CategoryFeatureGenerator(object):
    def __init__(self):
        self.dic = dict()
        self.dic['unk'] = 0
        self.counter = 1

    def register(self, key):
        '''
        Register record.
        '''
        if key not in self.dic:
            self.dic[key] = self.counter
            self.counter += 1

    def size(self):
        return len(self.dic)


    def gen(self, key):
        '''
        Generate one-hot representation for a record.
        '''
        if key not in self.dic:
            res = self.dic['unk']
        else:
            res = self.dic[key]
        return res

    def __repr__(self):
        return '<CategoryFeatureGenerator %d>' % len(self.dic)

feature_fields = ['user_id','user_location','content_id','cate_id','word','check_in_period']
feature_dict = {}
for key in feature_fields:
    feature_dict[key] = CategoryFeatureGenerator()

def __init_dataset__(path):
    with open(path, "r") as f:
        for line in f:
            user_id, time_period, user_location, app_id, content_id, cate_id, title, brief, quality_level, check_in_period, read_time = line.strip().split('\t')
            feature_dict['user_id'].register(user_id)
            feature_dict['content_id'].register(int(content_id))
            user_location_list = user_location.split('|')
            for ul in user_location_list:
                feature_dict['user_location'].register(ul)
                feature_dict['cate_id'].register(cate_id)
                title_list = title.split(' ')
            for w in title_list:
                feature_dict['word'].register(w.lower())
                brief_list = brief.split(' ')
            for w in brief_list:
                feature_dict['word'].register(w.lower())
                feature_dict['check_in_period'].register(int(check_in_period))

class ReaderData(object):
    def __init__(self, data_path, test_ratio, is_test):
        __init_dataset__(data_path)
        self.data_path = data_path
        self.test_ratio = test_ratio
        self.is_test = is_test
    
    def reader_creator(self):
        def reader():
            rand = random.Random()
            path = self.data_path
            test_ratio = self.test_ratio
            is_test = self.is_test
            with open(path, "r") as f:
                for line in f:
                    if (rand.random() < test_ratio) == is_test:
                        user_id, time_period, user_location, app_id, content_id, cate_id, title, brief, quality_level, check_in_period, read_time = line.strip().split('\t')
                        user_id_code = feature_dict['user_id'].gen(user_id)
                        user_location_code = [feature_dict['user_location'].gen(ul) for ul in user_location.split('|')]
                        content_id_code = feature_dict['content_id'].gen(int(content_id))
                        cate_id_code = feature_dict['cate_id'].gen(cate_id)
                        title_code = [feature_dict['word'].gen(w.lower()) for w in title.split(' ')]
                        brief_code = [feature_dict['word'].gen(w.lower()) for w in brief.split(' ')]
                        check_in_period_code = feature_dict['check_in_period'].gen(int(check_in_period))
                        record = [user_id_code, int(time_period), user_location_code, content_id_code, cate_id_code, title_code, brief_code, check_in_period_code]
                        yield record + [[float(read_time)]]
        return reader

    def get_content_word_dict(self):
        return feature_dict['word'].dic

    def user_id_len(self):
        return feature_dict['user_id'].size()

    def get_user_location_dict(self):
        return feature_dict['user_location'].dic

    def content_id_len(self):
        return feature_dict['content_id'].size()

    def category_id_len(self):
        return feature_dict['cate_id'].size()
   
    def check_in_period_len(self):
        return feature_dict['check_in_period'].size()

if __name__ == '__main__':
    path = "./videoSample"
    test_ratio = 0.1
    is_test = False
    trainer = ReaderData(path, test_ratio, is_test)
    print trainer.user_id_len()
    a = trainer.get_user_location_dict()
    for no, rcd in enumerate(trainer.read()):
        print no, rcd
        if no > 10 : break

训练模型的代码如下：

#!/usr/bin/python
#encoding=utf8

import paddle.v2 as paddle
import cPickle
import copy
from paddle.v2.dataset.video import feature_dict, ReaderData 

dataset_train = ReaderData("./videoSample", 0.1, False)

def get_usr_combined_features():
    uid = paddle.layer.data(
        name='user_id',
        type=paddle.data_type.integer_value(
            dataset_train.user_id_len()))
    usr_emb = paddle.layer.embedding(input=uid, size=32)
    usr_fc = paddle.layer.fc(input=usr_emb, size=32)

    time_period = paddle.layer.data(
	name='time_period', 
	type=paddle.data_type.integer_value(24))
    time_period_emb = paddle.layer.embedding(input=time_period, size=16)
    time_period_fc = paddle.layer.fc(input=time_period_emb, size=16)

    usr_location = paddle.layer.data(
	name='user_location',
	type=paddle.data_type.sparse_binary_vector(
	len(dataset_train.get_user_location_dict())))
    usr_location_fc = paddle.layer.fc(input=usr_location, size=32)

    usr_combined_features = paddle.layer.fc(
        input=[usr_fc, time_period_fc, usr_location_fc],
        size=200,
        act=paddle.activation.Tanh())
    return usr_combined_features


def get_content_combined_features():
    content_word_dict = dataset_train.get_content_word_dict()
    content_id = paddle.layer.data(
        name='content_id',
        type=paddle.data_type.integer_value(
            dataset_train.content_id_len()))
    content_emb = paddle.layer.embedding(input=content_id, size=32)
    content_fc = paddle.layer.fc(input=content_emb, size=32)

    content_categories = paddle.layer.data(
        name='category_id',
        type=paddle.data_type.integer_value(
            dataset_train.category_id_len()))
    content_categories_emb = paddle.layer.embedding(input=content_categories, size=16)
    content_categories_fc = paddle.layer.fc(input=content_categories, size=16)

    content_title_id = paddle.layer.data(
        name='title',
        type=paddle.data_type.integer_value_sequence(len(content_word_dict)))
    content_title_emb = paddle.layer.embedding(input=content_title_id, size=32)
    content_title_conv = paddle.networks.sequence_conv_pool(
        input=content_title_emb, hidden_size=32, context_len=2)

    content_brief_id = paddle.layer.data(
	name='brief',
	type=paddle.data_type.integer_value_sequence(len(content_word_dict)))
    content_brief_emb = paddle.layer.embedding(input=content_brief_id, size=32)
    content_brief_conv = paddle.networks.sequence_conv_pool(
	input=content_brief_emb, hidden_size=32, context_len=2)

    check_in_period = paddle.layer.data(
	name='check_in_period',
	type=paddle.data_type.integer_value(
	    dataset_train.check_in_period_len()))
    check_in_period_emb = paddle.layer.embedding(input=check_in_period, size=32)
    check_in_period_fc = paddle.layer.fc(input=check_in_period, size=32)

    content_combined_features = paddle.layer.fc(
        input=[content_fc, content_categories_fc, content_title_conv, content_brief_conv, check_in_period_fc],
        size=200,
        act=paddle.activation.Tanh())
    return content_combined_features


def main():
    paddle.init(use_gpu=False)
    usr_combined_features = get_usr_combined_features()
    content_combined_features = get_content_combined_features()
    inference = paddle.layer.cos_sim(
        a=usr_combined_features, b=content_combined_features, size=1, scale=5)
    cost = paddle.layer.mse_cost(
        input=inference,
        label=paddle.layer.data(
            name='read_time', type=paddle.data_type.dense_vector(1)))

    parameters = paddle.parameters.create(cost)

    trainer = paddle.trainer.SGD(
        cost=cost,
        parameters=parameters,
        update_equation=paddle.optimizer.Adam(learning_rate=1e-4))
    feeding = {
        'user_id': 0,
        'time_period': 1,
        'user_location': 2,
        'content_id': 3,
        'category_id': 4,
        'title': 5,
        'brief': 6,
	'check_in_period': 7,
	'read_time': 8
    }

    def event_handler(event):
        if isinstance(event, paddle.event.EndIteration):
            if event.batch_id % 100 == 0:
                print "Pass %d Batch %d Cost %.2f" % (
                    event.pass_id, event.batch_id, event.cost)

    trainer.train(
        reader=paddle.batch(
            paddle.reader.shuffle(
                dataset_train.reader_creator(), buf_size=8192),
            batch_size=256),
        event_handler=event_handler,
        feeding=feeding,
        num_passes=1)

    user_id = "123a"
    content_id = 20419555
    time_period = 16
    user_location = "上海市|上海市"
    cate_id = "1001"
    title = "白鹿原 白嘉轩 娶 的 第七任 老婆 仙草 洞房花烛 夜 白嘉轩 跑 了"
    brief = ""
    check_in_period = 3600

    user_id = feature_dict['user_id'].gen(user_id)
    content_id_code = feature_dict['content_id'].gen(content_id)
    cate_id_code = feature_dict['cate_id'].gen(cate_id)
    title_code = [feature_dict['word'].gen(w.lower()) for w in title.split(' ')]
    brief_code = [feature_dict['word'].gen(w.lower()) for w in brief.split(' ')]
    check_in_period_code = feature_dict['check_in_period'].gen(int(check_in_period))

    print [user_id, content_id, time_period, user_location, cate_id, title, brief, check_in_period]
    feature = [user_id_code, int(time_period), user_location_code, content_id_code, cate_id_code, title_code, brief_code, check_in_period_code]
    print feature

    infer_dict = copy.copy(feeding)
    del infer_dict['read_time']

    prediction = paddle.infer(
        output_layer=inference,
        parameters=parameters,
        input=[feature],
        feeding=infer_dict)
    print prediction


if __name__ == '__main__':
    main()

[kuke]

Could you please typeset your issue into a better format? Others may read your code more easily and offer useful help.

[Yancey1989]

The parameter of paddle.reader.shuffle is a callable interface, so you can define the reader as:

class ReaderData(object):
    ...
    def reader_creator(self):
        def reader():
            ...
        return reader

And call the reader as:

trainer.train(
    reader=paddle.batch(
        paddle.reader.shuffle(
            dataset_train.reader_creator(), buf_size=8192),
        batch_size=256),

[xlhlhlx]

代码改成如上之后上一个问题解决了，但又报其它错了，且没有定位到我的代码里面哪一行，还请帮忙定位一下，上面的代码已经是最新的，报错信息如下：

[xlhlhlx]

生成的训练数据的samle如下：
[14, 10, [20, 24], 14, 2, [146, 45, 110, 111, 112, 147, 148, 149, 85, 150, 151, 83, 152, 153, 83, 154, 155], [12], 14, [121539.0]]
[13, 4, [22, 23], 13, 8, [138, 139, 140, 141, 142, 143, 144, 85, 145, 83], [12], 13, [4349.0]]

[lcy-seso]

这里有错：

content_categories = paddle.layer.data(
        name='category_id',
        type=paddle.data_type.integer_value(
            dataset_train.category_id_len()))
    content_categories_emb = paddle.layer.embedding(input=content_categories, size=16)
    content_categories_fc = paddle.layer.fc(input=content_categories, size=16)

content_categories_fc = paddle.layer.fc(input=content_categories, size=16) 的输入给错了。

[xlhlhlx]

thx,fixed