Tensorflow功能列用于变量值列表 [英] Tensorflow feature column for variable list of values

问题描述

从TensorFlow文档中可以很明显地看出如何使用tf.feature_column.categorical_column_with_vocabulary_list创建功能列，该功能列将一些字符串作为输入并输出一个热向量.例如

From the TensorFlow docs it's clear how to use tf.feature_column.categorical_column_with_vocabulary_list to create a feature column which takes as input some string and outputs a one-hot vector. For example

vocabulary_feature_column =
    tf.feature_column.categorical_column_with_vocabulary_list(
        key="vocab_feature",
        vocabulary_list=["kitchenware", "electronics", "sports"])

比方说，"kitchenware"映射到[1,0,0]，而"electronics"映射到[0,1,0].我的问题与将字符串列表作为一项功能有关.例如，如果特征值为["kitchenware","electronics"]，则所需的输出将为[1,1,0].输入列表的长度不是固定的，但输出尺寸是固定的.

Let's say "kitchenware" maps to [1,0,0] and "electronics" maps to [0,1,0]. My question is related to having a list of strings as a feature. For example, if the feature value was ["kitchenware","electronics"] then the desired output would be [1,1,0]. The input list length is not fixed but the output dimension is.

用例是一个直接的单词袋型模型(显然具有更大的词汇表！).

The use case is a straight bag-of-words type model (obviously with a much larger vocabulary list!).

实现此目的的正确方法是什么?

What is the correct way to implement this?

推荐答案

您应该使用tf.feature_column.indicator_column 参见 https://www.tensorflow.org/versions/master /api_docs/python/tf/feature_column/indicator_column

you should use tf.feature_column.indicator_column see https://www.tensorflow.org/versions/master/api_docs/python/tf/feature_column/indicator_column

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