Question

我是Tensorflow Estimator的新手。我想知道是否可以将分类变量数组作为特征传递给估计器，然后自动将其转换为嵌入数组。例如，以下是CSV文件中的记录。它包含2个类别变量列表（括在方括号中）“国家”和“监视”，2个类别变量“ day_of_week”和“ day_period”以及一个目标“ movie_id”。

day_of_week,day_period,country,movie_id,watched
SUNDAY,EVENING,[USA,UK],B2JO1owWbeLn,[WGdZ5qZmLw0,abcdef]
MONDAY,EVENING,[China],xxx,[abc,def,ijk]

根据文档https://www.tensorflow.org/api_docs/python/tf/feature_column，“ day_of_week”和“ day_period”可以表示为“ categorical_column_with_vocabulary_list”。这很简单。但是，“国家”和“监视”是类别变量的列表。我想将列表中的每个分类变量合并到一个嵌入中。从同一文档中，“ tf.feature_column.embedding_column”就可以解决问题。

以下功能构建代表上述输入的列。

def build_model_columns():
    day_of_week = tf.feature_column.categorical_column_with_vocabulary_list('day_of_week', day_of_weeks)
    day_period = tf.feature_column.categorical_column_with_vocabulary_list('day_period', day_periods)
    country = tf.feature_column.categorical_column_with_vocabulary_list('country', countries)
    watched = tf.feature_column.categorical_column_with_vocabulary_list('watched', movie_emb_ids)

    columns = [
        tf.feature_column.indicator_column(day_of_week),
        tf.feature_column.indicator_column(day_period),
        tf.feature_column.embedding_column(country, 8),
        tf.feature_column.embedding_column(watched, 32)
    ]
    return columns

以下是生成训练数据集的函数

def tensor_to_array(tensor):
    length = tf.size(tf.string_split([tensor], ""))
    sub = tf.substr(tensor, 1, length-2) # remove the leading '[' and trailing ']'
    splits = tf.string_split([sub], delimiter=',')
    return splits

def train_input_fn():
    train_files = "train.csv"
    target_files = "target.csv"
    target_table, target_ids = read_table_lookup(target_files, "movie")

    def preprocess(day_of_week, day_period, country, movie_id, watched):

        features = {
            'day_of_week': day_of_week,
            'day_period': day_period,
            'country': tensor_to_array(country),
            'watched': tensor_to_array(watched)

        }
        # target_table is a lookup table converting "movie_id" to integer "id"
        return features, target_table.lookup(movie_id) 

    dataset = (tf.contrib.data.CsvDataset(train_files, record_defaults, header=True)
           .map(preprocess, num_parallel_calls=5)
           .batch(batch_size=batch_size, drop_remainder=False)
           .repeat()
          )

    # iterator = dataset.make_initializable_iterator()
    # tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)

    return dataset

以下是用于创建和训练估算器的代码段。

hidden_units = [512, 512]
record_defaults = [[""]] * 5
columns = build_model_columns()
estimator = tf.estimator.DNNClassifier(model_dir="dir",
                                   feature_columns=columns,
                                   hidden_units=hidden_units,
                                   n_classes=len(target_ids)) # length of all targets

estimator.train(input_fn=train_input_fn)

我没有任何错误，似乎一切都可以按预期进行，但是训练损失却很大，在3，xxx左右波动并且从未减少。见下文

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into /home/cocoza4/movie_models/deep/model.ckpt.
INFO:tensorflow:loss = 6538.0645, step = 0
INFO:tensorflow:global_step/sec: 17.353
INFO:tensorflow:loss = 3596.562, step = 100 (5.764 sec)
INFO:tensorflow:global_step/sec: 17.434
INFO:tensorflow:loss = 3504.936, step = 200 (5.736 sec)
INFO:tensorflow:global_step/sec: 17.4234
INFO:tensorflow:loss = 3500.0488, step = 300 (5.739 sec)
INFO:tensorflow:global_step/sec: 17.5321
INFO:tensorflow:loss = 3480.702, step = 400 (5.705 sec)
INFO:tensorflow:global_step/sec: 17.4534
INFO:tensorflow:loss = 3517.599, step = 500 (5.729 sec)
INFO:tensorflow:global_step/sec: 17.3421
INFO:tensorflow:loss = 3446.142, step = 600 (5.769 sec)
INFO:tensorflow:global_step/sec: 17.313
INFO:tensorflow:loss = 3281.3088, step = 700 (5.776 sec)
INFO:tensorflow:global_step/sec: 17.4421
INFO:tensorflow:loss = 3326.7336, step = 800 (5.731 sec)
INFO:tensorflow:global_step/sec: 17.3619
INFO:tensorflow:loss = 3464.902, step = 900 (5.762 sec)
INFO:tensorflow:global_step/sec: 17.2013
INFO:tensorflow:loss = 3364.2153, step = 1000 (5.813 sec)
INFO:tensorflow:global_step/sec: 17.4429
INFO:tensorflow:loss = 3410.449, step = 1100 (5.734 sec)
INFO:tensorflow:global_step/sec: 17.0483
INFO:tensorflow:loss = 3351.018, step = 1200 (5.866 sec)
INFO:tensorflow:global_step/sec: 17.4214
INFO:tensorflow:loss = 3386.995, step = 1300 (5.740 sec)
INFO:tensorflow:global_step/sec: 17.7965
INFO:tensorflow:loss = 3263.6074, step = 1400 (5.617 sec)
INFO:tensorflow:global_step/sec: 17.6944
INFO:tensorflow:loss = 3321.574, step = 1500 (5.652 sec)
INFO:tensorflow:global_step/sec: 17.3603
INFO:tensorflow:loss = 3234.7761, step = 1600 (5.760 sec)

我想知道在准备训练数据时是否做错了什么？

谢谢

Peeranat F。

Answer 1

引起我注意的第一件事是隐藏单元的数量。我会尝试调整您的隐藏单位。通常，各层的大小应减小，因此我会尝试[512,256,128]

Tensorflow Estimator：使用tf.feature_column.embedding_column作为类别变量列表时的损失不会减少

1 个答案: