Question

Is there something big broken in the tensorflow framework or am I just making some simple mistakes here. I tried to get the GMM or KMeans clustering to work but I'm totally stuck.

https://pastebin.com/eNxs5mUQ

import numpy as np
import tensorflow as tf

from tensorflow.contrib.learn.python.learn.estimators.kmeans import KMeansClustering

def make_random_centers(num_centers, num_dims):
  return np.round(np.random.rand(num_centers,
                                 num_dims).astype(np.float32) * 500)

def make_random_points(centers, num_points):
  num_centers, num_dims = centers.shape
  assignments = np.random.choice(num_centers, num_points)
  offsets = np.round(np.random.randn(num_points,
                                     num_dims).astype(np.float32) * 20)
  points = centers[assignments] + offsets
  return points

num_centers = 3
num_dims = 2
num_points = 100

true_centers = make_random_centers(num_centers, num_dims)
points = make_random_points(true_centers, num_points)
print(points.shape)
print(points.dtype)

km = KMeansClustering(num_centers)
km.fit(x=points)
clusters = km.clusters()
print(clusters)

I'm getting a InvalidArgumentError though my data seems to be the correct shape and type (= (100, 2), float32):

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input' with dtype float and shape [?,2]

Answer 1

尝试使用def get_input_fn(x): def input_fn(): return tf.constant(x.astype('float32')), None return input_fn这样的

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Tensorflow KMeans and GMM broken?

1 个答案: