Python ValueError:feature_列的项必须是DenseColumn或Category列

代码很简单：

    x_data = np.linspace(0, 10.0, 1000000)
    y_true = (0.5 * x_data) + 5 
    x_train, x_eval, y_train, y_eval = train_test_split(x_data, y_true, test_size = 0.25, random_state=101)
    input_func = tf.estimator.inputs.numpy_input_fn({'x':x_train}, y_train, 
                                                batch_size=8, num_epochs=None, shuffle= True)
    estimator = tf.estimator.LinearRegressor(feature_columns=feat_cols)
    estimator.train(input_fn=input_func, steps=1000)

错误：

信息：tensorflow:正在调用模型\u fn。 --------------------------------------ValueError回溯最近的调用 最后的 -->1个估计器。列车输入\u fn=输入\u func，步长=1000 2评估指标=估计器。评估输入=评估输入功能，步骤=1000

8帧 /usr/local/lib/python3.6/dist-packages/tensorflow/python/feature\u column/feature\u column\u v2.py 在initself中，特征列、单元、稀疏组合器、可训练、， 姓名**kwargs 498升值错误 499“要素_列的项目必须为a” ->500’密度柱或分类柱。给定：{}.formatcolumn 501 502自身单位=单位

ValueError:feature_列的项必须是DenseColumn或 分类列。给定：SequenceNumericColumnkey='x'，shape=1，， 默认值=0.0，数据类型=tf.float32，规格化器=None

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以下代码适用于训练和预测

x_data = np.linspace(0, 10.0, 1000)
print(x_data.shape)
y_true = (0.5 * x_data) + 5
print(y_true.shape)
x_train, x_eval, y_train, y_eval = train_test_split(x_data, y_true, test_size=0.25, random_state=101)
train_func = tf.estimator.inputs.numpy_input_fn({'x': x_train}, y_train, batch_size=8, num_epochs=10, shuffle=True)

features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
estimator = tf.estimator.LinearRegressor(feature_columns=features)

estimator.train(input_fn=train_func, steps=100) # Fit the model to training data.

eval_func = tf.estimator.inputs.numpy_input_fn({'x': x_eval}, batch_size=1, num_epochs=1, shuffle=False)

result = estimator.predict(eval_func) # Predict scores

print("predict_scores", list(result))