python - K 均值聚类误差的 Tensorflow 输入函数

Question

这是一个简单的代码，用于创建 tensorflow 图来聚类虹膜数据。它使用 tf.estimator.inputs.numpy_input_fn 为 tf.contrib.learn.KMeansClustering 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)

你知道我为什么会收到此错误以及如何调试它吗？

虹膜.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

您无法将 tf.estimator.inputs.numpy_input_fn 传递到任何 tf.contrib 类中，因为它不返回特征、标签，而是返回封装两者的东西。

当使用旧的 contrib 类时，最好的选择是自己编写输入函数。方法如下:

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