Question

我正在尝试使用两个功能在输入上运行DNNRegressor，其中一个功能是分类的。我想我需要在一个热门编码中表示这个输入，但我很难设置feature_columns（错误如下所示）。

如何使用提供的样本数据训练DNNRegressor（在下面的代码中）？

import tensorflow as tf
import pandas as pd

def main():

    data = pd.DataFrame({
        "make": ["Mazda", "Mazda", "Mazda", "Toyota", "Toyota", "Toyota"],
        "years_old": [0, 2, 5, 0, 2, 5],
        "price": [20000, 12000, 8000, 18000, 11000, 8000]
    })

    def get_train_inputs():
        feature_columns = {
            "make": tf.constant(data["make"].values),
            "years_old": tf.constant(data["years_old"].values)
        }
        labels = tf.constant(data["price"].values)
        return feature_columns, labels

    feature_columns = [
        tf.contrib.layers.one_hot_column("make"),
        tf.contrib.layers.real_valued_column("years_old")
    ]

    regressor = tf.contrib.learn.DNNRegressor(
        feature_columns=feature_columns,
        hidden_units=[10, 10]
    )

    regressor.fit(input_fn=get_train_inputs, steps=2)

if __name__ == "__main__":
    main()

我在这段代码中遇到的错误：

Traceback (most recent call last):
  File "/app/run.py", line 33, in <module>
    main()
  File "/app/run.py", line 30, in main
    regressor.fit(input_fn=get_train_inputs, steps=2)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 281, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 430, in fit
    loss = self._train_model(input_fn=input_fn, hooks=hooks)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 927, in _train_model
    model_fn_ops = self._get_train_ops(features, labels)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1132, in _get_train_ops
    return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1103, in _call_model_fn
    model_fn_results = self._model_fn(features, labels, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py", line 132, in _dnn_model_fn
    scope=input_layer_scope)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 249, in input_from_feature_columns
    default_name='input_from_feature_columns')
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 159, in _input_from_feature_columns
    default_name=column.name,
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py", line 817, in name
    return "{}_one_hot".format(self.sparse_id_column.name)
AttributeError: 'str' object has no attribute 'name'

Answer 1

您不应将字符串作为one_hot_column的参数传递。使用“feature_column.sparse_column_with_hash_bucket”（或种类）如下：

sparse_column = feature_column.sparse_column_with_hash_bucket(
    'make', hash_bucket_size=6)
feature_columns = [
    feature_column.one_hot_column(sparse_column),
    feature_column.real_valued_column('years_old')
]

而不是

feature_columns = [
    tf.contrib.layers.one_hot_column("make"),
    tf.contrib.layers.real_valued_column("years_old")
]

输入中的分类变量

1 个答案: