python - Tensorflow:如何为Numpy矩阵输入创建feature_columns

我使用的是tensorflow 1.8.0，python 3.6.5。
数据是iris数据集。代码如下：

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import tensorflow as tf

X = iris['data']
y = iris['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

input_train=tf.estimator.inputs.numpy_input_fn(x=X_train,
            y=y_train, num_epochs=100, shuffle=False)
classifier_model = tf.estimator.DNNClassifier(hidden_units=[10,
                  20, 10], n_classes=3, feature_columns=??)

这是我的问题，如何为numpy矩阵设置feature_列？
如果我将X和y转换为pandas.DataFrame，我可以为feature_列使用以下代码，它在DNNClassifier模型中工作。

features = X.columns
feature_columns = [tf.feature_column.numeric_column(key=key) for key in features]

最佳答案

您可以将numpy ndarray包装在字典中，并将其作为输入传递给numpy_input_fn方法，然后使用字典中的键定义您的x。还要注意，因为feature_column中的每个数据都有4个维度，所以在定义X_train时需要指定shape参数。下面是完整的代码：

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import tensorflow as tf

iris = load_iris()

X = iris['data']
y = iris['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

input_train = tf.estimator.inputs.numpy_input_fn(
                  x = {'x': X_train},
                  y = y_train,
                  num_epochs = 100,
                  shuffle = False)

feature_columns = [tf.feature_column.numeric_column(key='x', shape=(X_train.shape[1],))]

classifier_model = tf.estimator.DNNClassifier(
                       hidden_units=[10, 20, 10],
                       n_classes=3,
                       feature_columns=feature_columns)