用于 K 均值聚类误差的 Tensorflow 输入函数

Question

这是创建张量流图以聚类鸢尾花数据的简单代码。它使用 tf.estimator.inputs.numpy_input_fn 为 tf.contrib.learn.KMeansClustering k 均值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Tensorflowreturns出现如下错误：

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

你知道我为什么会收到这个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

Answer 1

您不能将 tf.estimator.inputs.numpy_input_fn 传递给任何 tf.contrib 类，因为它没有 return 特征、标签，而是封装了两者的东西。

使用较旧的贡献类时，最好的选择是自己编写输入函数。是这样的：

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

用于 K 均值聚类误差的 Tensorflow 输入函数

Tensorflow input function for K-means clustering error

python

tensorflow

machine-learning

k-means

tensorflow-estimator