ValueError:功能不在功能字典中

问题描述

我正在尝试使用TensorFlow编写一个简单的深度机器学习模型.我使用的是我在Excel中制作的玩具数据集，目的是使模型正常工作并接受数据.我的代码如下:

I am attempting to write a simple deep machine learning model using TensorFlow.I'm using a toy dataset I made up in Excel just to get the model working and accepting data. My code is as follows:

import pandas as pd
import numpy as np
import tensorflow as tf

raw_data = np.genfromtxt('ai/mock-data.csv', delimiter=',', dtype=str)
my_data = np.delete(raw_data, (0), axis=0) #deletes the first row, axis=0 indicates row, axis=1 indicates column
my_data = np.delete(my_data, (0), axis=1) #deletes the first column

policy_state = tf.feature_column.categorical_column_with_vocabulary_list('policy_state', [
    'AL', 'CA', 'MI'
])

modern_classic_ind = tf.feature_column.categorical_column_with_vocabulary_list('modern_classic_ind', [
    '0', '1'
])

h_plus_ind = tf.feature_column.categorical_column_with_vocabulary_list('h_plus_ind', [
    '0', '1'
])

retention_ind = tf.feature_column.categorical_column_with_vocabulary_list('retention_ind', [
    '0', '1'
])

feature_columns = [
    tf.feature_column.indicator_column(policy_state),
    tf.feature_column.indicator_column(modern_classic_ind),
    tf.feature_column.indicator_column(h_plus_ind)
]
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                      hidden_units=[10, 20, 10],
                                      n_classes=3,
                                      model_dir="/tmp/ret_model")

train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(my_data[:, 0:3], dtype=str)},
y=np.array(np.array(my_data[:, 3], dtype=str)),
num_epochs=None,
shuffle=True)

classifier.train(input_fn=train_input_fn, steps=2000)

不幸的是，我收到以下错误.我尝试过修剪csv文件中的标签，而不是保留标签，将要素列命名为其他内容，并更改numpy数组的类型.错误仍然存​​在.

Unfortunately, I am getting the following error. I have tried trimming the labels off the csv file versus leaving them, naming the feature columns different things, and changing the type of the numpy array. The error persists.

ValueError:功能h_plus_ind不在功能字典中.

如果我删除 h_plus_ind ，它只会将错误抛出在另一列上.

If I remove h_plus_ind, it simply throws the error on a different column.

推荐答案

在使用 tf.feature_columns 时，输入input_fn中的数据应具有与先前创建的要素列相同的键.因此，您的 train_input_fn 的 x 应该是字典，其键以 feature_columns 命名.

When using tf.feature_columns, the data you feed in your input_fn should have the same keys as the feature columns previously created.So, the x of your train_input_fn should be a dictionary, with keys named after the feature_columns.

一个模拟示例:

x = {"policy_state": np.array(['AL','AL','AL','AL','AL']),
     "modern_classic_ind": np.array(['0','0','0','0','0']),
     "h_plus_ind": np.array(['0','0','0','0','0']),}

在侧面:

来自开发者Google博客的这篇很棒的文章可能是非常好的阅读，因为它介绍了使用 tf.Dataset API直接从csv文件直接创建 input_fn 的新方法.它具有更好的内存管理，并且避免将所有数据集加载到内存中.

This great article from the developers google blog could be a great read, as it introduces a new way to create input_fn directly from a csv file with the tf.Dataset API. It has a better memory management, and avoid loading all the dataset into memory.

这篇关于ValueError:功能不在功能字典中的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！