Kmeans聚类如何在张量流中工作？

我看到在tensorflow contrib库中有一个Kmeans集群的实现。但是，我无法简单地估算2D点的聚类中心。

码：

## Generate synthetic data
N,D = 1000, 2 # number of points and dimenstinality

means = np.array([[0.5, 0.0],
                  [0, 0],
                  [-0.5, -0.5],
                  [-0.8, 0.3]])
covs = np.array([np.diag([0.01, 0.01]),
                 np.diag([0.01, 0.01]),
                 np.diag([0.01, 0.01]),
                 np.diag([0.01, 0.01])])
n_clusters = means.shape[0]

points = []
for i in range(n_clusters):
    x = np.random.multivariate_normal(means[i], covs[i], N )
    points.append(x)
points = np.concatenate(points)

## construct model
kmeans = tf.contrib.learn.KMeansClustering(num_clusters = n_clusters)
kmeans.fit(points.astype(np.float32))

我收到以下错误：

InvalidArgumentError (see above for traceback): Shape [-1,2] has negative dimensions
     [[Node: input = Placeholder[dtype=DT_FLOAT, shape=[?,2], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

我想我做错了什么，但无法从文档中找出答案。

编辑：

我使用来解决它，input_fn但是它确实很慢（我不得不将每个群集中的点数减少到10以查看结果）。为什么会这样，我如何使其更快？

 def input_fn():
    return tf.constant(points, dtype=tf.float32), None

## construct model
kmeans = tf.contrib.learn.KMeansClustering(num_clusters = n_clusters, relative_tolerance=0.0001)
kmeans.fit(input_fn=input_fn)
centers = kmeans.clusters()
print(centers)

解决了：

似乎应该设定相对公差。所以我只更改了一条线，效果很好。kmeans = tf.contrib.learn.KMeansClustering(num_clusters = n_clusters, relative_tolerance=0.0001)