Python K-均值聚类误差的Tensorflow输入函数

这是一个创建tensorflow图以对iris数据进行聚类的简单代码。它使用

tf.estimator.inputs.numpy\u input\u fn

为

tf.contrib.learn.kmeans聚类

k-means聚类器定义输入函数

import os
import numpy as np
import tensorflow as tf

# Data set
IRIS = "iris.csv"

# Load datasets
iris = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS,
    target_dtype=np.int,
    features_dtype=np.float32)

# Build KMeans Clustering model.
num_clusters = 4
estimator = tf.contrib.learn.KMeansClustering(
    num_clusters,
    model_dir="/tmp/iris_model",
    initial_clusters='random',
    distance_metric='squared_euclidean',
    use_mini_batch=True)

# Define the training inputs
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": np.array(iris.data)},
    y=np.array(iris.target),
    batch_size=4,
    num_epochs=None,
    shuffle=True)

# Fit model.
clusters = estimator.fit(input_fn=input_fn)

但是Tensorflow返回以下错误：

...
AssertionError: Tensor("random_shuffle_queue_DequeueMany:2", shape=(4,), dtype=int64)

您知道我为什么会出现此错误以及如何调试它吗

iris.csv：

150,4,setosa,versicolor,virginica
6.4,2.8,5.6,2.2,2
5.0,2.3,3.3,1.0,1
4.9,2.5,4.5,1.7,2
4.9,3.1,1.5,0.1,0
5.7,3.8,1.7,0.3,0
4.4,3.2,1.3,0.2,0
5.4,3.4,1.5,0.4,0
6.9,3.1,5.1,2.3,2
6.7,3.1,4.4,1.4,1
5.1,3.7,1.5,0.4,0
5.2,2.7,3.9,1.4,1
6.9,3.1,4.9,1.5,1
5.8,4.0,1.2,0.2,0
5.4,3.9,1.7,0.4,0
7.7,3.8,6.7,2.2,2
6.3,3.3,4.7,1.6,1
6.8,3.2,5.9,2.3,2
7.6,3.0,6.6,2.1,2
6.4,3.2,5.3,2.3,2
5.7,4.4,1.5,0.4,0
6.7,3.3,5.7,2.1,2
6.4,2.8,5.6,2.1,2
5.4,3.9,1.3,0.4,0
6.1,2.6,5.6,1.4,2
7.2,3.0,5.8,1.6,2
5.2,3.5,1.5,0.2,0
5.8,2.6,4.0,1.2,1
5.9,3.0,5.1,1.8,2
5.4,3.0,4.5,1.5,1
6.7,3.0,5.0,1.7,1
6.3,2.3,4.4,1.3,1
5.1,2.5,3.0,1.1,1
6.4,3.2,4.5,1.5,1
6.8,3.0,5.5,2.1,2
6.2,2.8,4.8,1.8,2
6.9,3.2,5.7,2.3,2
6.5,3.2,5.1,2.0,2
5.8,2.8,5.1,2.4,2
5.1,3.8,1.5,0.3,0
4.8,3.0,1.4,0.3,0
7.9,3.8,6.4,2.0,2
5.8,2.7,5.1,1.9,2
6.7,3.0,5.2,2.3,2
5.1,3.8,1.9,0.4,0
4.7,3.2,1.6,0.2,0
6.0,2.2,5.0,1.5,2
4.8,3.4,1.6,0.2,0
7.7,2.6,6.9,2.3,2
4.6,3.6,1.0,0.2,0
7.2,3.2,6.0,1.8,2
5.0,3.3,1.4,0.2,0
6.6,3.0,4.4,1.4,1
6.1,2.8,4.0,1.3,1
5.0,3.2,1.2,0.2,0
7.0,3.2,4.7,1.4,1
6.0,3.0,4.8,1.8,2
7.4,2.8,6.1,1.9,2
5.8,2.7,5.1,1.9,2
6.2,3.4,5.4,2.3,2
5.0,2.0,3.5,1.0,1
5.6,2.5,3.9,1.1,1
6.7,3.1,5.6,2.4,2
6.3,2.5,5.0,1.9,2
6.4,3.1,5.5,1.8,2
6.2,2.2,4.5,1.5,1
7.3,2.9,6.3,1.8,2
4.4,3.0,1.3,0.2,0
7.2,3.6,6.1,2.5,2
6.5,3.0,5.5,1.8,2
5.0,3.4,1.5,0.2,0
4.7,3.2,1.3,0.2,0
6.6,2.9,4.6,1.3,1
5.5,3.5,1.3,0.2,0
7.7,3.0,6.1,2.3,2
6.1,3.0,4.9,1.8,2
4.9,3.1,1.5,0.1,0
5.5,2.4,3.8,1.1,1
5.7,2.9,4.2,1.3,1
6.0,2.9,4.5,1.5,1
6.4,2.7,5.3,1.9,2
5.4,3.7,1.5,0.2,0
6.1,2.9,4.7,1.4,1
6.5,2.8,4.6,1.5,1
5.6,2.7,4.2,1.3,1
6.3,3.4,5.6,2.4,2
4.9,3.1,1.5,0.1,0
6.8,2.8,4.8,1.4,1
5.7,2.8,4.5,1.3,1
6.0,2.7,5.1,1.6,1
5.0,3.5,1.3,0.3,0
6.5,3.0,5.2,2.0,2
6.1,2.8,4.7,1.2,1
5.1,3.5,1.4,0.3,0
4.6,3.1,1.5,0.2,0
6.5,3.0,5.8,2.2,2
4.6,3.4,1.4,0.3,0
4.6,3.2,1.4,0.2,0
7.7,2.8,6.7,2.0,2
5.9,3.2,4.8,1.8,1
5.1,3.8,1.6,0.2,0
4.9,3.0,1.4,0.2,0
4.9,2.4,3.3,1.0,1
4.5,2.3,1.3,0.3,0
5.8,2.7,4.1,1.0,1
5.0,3.4,1.6,0.4,0
5.2,3.4,1.4,0.2,0
5.3,3.7,1.5,0.2,0
5.0,3.6,1.4,0.2,0
5.6,2.9,3.6,1.3,1
4.8,3.1,1.6,0.2,0
6.3,2.7,4.9,1.8,2
5.7,2.8,4.1,1.3,1
5.0,3.0,1.6,0.2,0
6.3,3.3,6.0,2.5,2
5.0,3.5,1.6,0.6,0
5.5,2.6,4.4,1.2,1
5.7,3.0,4.2,1.2,1
4.4,2.9,1.4,0.2,0
4.8,3.0,1.4,0.1,0
5.5,2.4,3.7,1.0,1
5.9,3.0,4.2,1.5,1
6.9,3.1,5.4,2.1,2
5.1,3.3,1.7,0.5,0
6.0,3.4,4.5,1.6,1
5.5,2.5,4.0,1.3,1
6.2,2.9,4.3,1.3,1
5.5,4.2,1.4,0.2,0
6.3,2.8,5.1,1.5,2
5.6,3.0,4.1,1.3,1
6.7,2.5,5.8,1.8,2
7.1,3.0,5.9,2.1,2
4.3,3.0,1.1,0.1,0
5.6,2.8,4.9,2.0,2
5.5,2.3,4.0,1.3,1
6.0,2.2,4.0,1.0,1
5.1,3.5,1.4,0.2,0
5.7,2.6,3.5,1.0,1
4.8,3.4,1.9,0.2,0
5.1,3.4,1.5,0.2,0
5.7,2.5,5.0,2.0,2
5.4,3.4,1.7,0.2,0
5.6,3.0,4.5,1.5,1
6.3,2.9,5.6,1.8,2
6.3,2.5,4.9,1.5,1
5.8,2.7,3.9,1.2,1
6.1,3.0,4.6,1.4,1
5.2,4.1,1.5,0.1,0
6.7,3.1,4.7,1.5,1
6.7,3.3,5.7,2.5,2
6.4,2.9,4.3,1.3,1

---

您不能将tf.estimator.inputs.numpy_input_fn传递到任何tf.contrib类中，因为它不返回特性和标签，而是封装了这两个特性和标签

使用较旧的contrib类时，最好的选择是自己编写输入函数。这就是为什么：

def make_numpy_input_fn(x, y, batch_size):
  def input_fn():
    features, labels = tf.train.shuffle_batch(
                             [tf.constant(x), tf.constant(y)],
                             batch_size=batch_size, 
                             capacity=50*batch_size,
                             min_after_dequeue=20*batch_size,
                             enqueue_many=True)
    features = {'x': features}
    return features, labels
  return input_fn

---

您不能将tf.estimator.inputs.numpy_input_fn传递到任何tf.contrib类中，因为它不返回特性和标签，而是封装了这两个特性和标签

使用较旧的contrib类时，最好的选择是自己编写输入函数。这就是为什么：

def make_numpy_input_fn(x, y, batch_size):
  def input_fn():
    features, labels = tf.train.shuffle_batch(
                             [tf.constant(x), tf.constant(y)],
                             batch_size=batch_size, 
                             capacity=50*batch_size,
                             min_after_dequeue=20*batch_size,
                             enqueue_many=True)
    features = {'x': features}
    return features, labels
  return input_fn