在尝试通过提要传递参数时，如何解决Tensorflow获取参数错误？

下面是我正在运行的代码，其中我正在实现一篇论文。我取两个矩阵，相乘，然后执行聚类。我做错了什么

import tensorflow as tf
from sklearn.cluster import KMeans
import numpy as np

a = np.random.rand(10,10)
b = np.random.rand(10,5)
F = tf.placeholder("float", [None, 10], name='F')
mask = tf.placeholder("float", [None, 5], name='mask')

def getZfeature(F,mask):
    return tf.matmul(F,mask)

def cluster(zfeature):    
    #km = KMeans(n_clusters=3)
    #km.fit(x)
    #mu = km.cluster_centers_
    return zfeature

def computeQ(zfeature,mu):
    print "computing q matrix"
    print type(zfeature), type(mu)

#construct model
zfeature = getZfeature(F,mask)
mu = cluster(zfeature)
q = computeQ(zfeature,mu)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    sess.run(q, feed_dict={F: a, mask: b})

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工作代码如下。你的问题是q和mu什么都不做。q是对函数computeQ的引用，因为它不返回任何内容。mu没有做任何事情，所以在这个答案中，我已经评估了zfeature。您可以在这两个函数中执行更多的张量运算，但需要返回一个张量才能使其工作

import tensorflow as tf
from sklearn.cluster import KMeans
import numpy as np

a = np.random.rand(10,10)
b = np.random.rand(10,5)
F = tf.placeholder("float", [None, 10], name='F')
mask = tf.placeholder("float", [None, 5], name='mask')

def getZfeature(F,mask):
    return tf.matmul(F,mask)

def cluster(zfeature):
    #km = KMeans(n_clusters=3)
    #km.fit(x)
    #mu = km.cluster_centers_
    return zfeature

def computeQ(zfeature,mu):
    print ("computing q matrix")
    print (type(zfeature), type(mu))

#construct model
zfeature = getZfeature(F,mask)
mu = cluster(zfeature)
q = computeQ(zfeature,mu)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    result=sess.run(zfeature, feed_dict={F: a, mask: b})
    print(result)

---

下面是使用tensorflow的k-means聚类代码

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错误消息将使人们更容易帮助您。您可能不知道，如果有人帮助过您，您可以选择答案。我注意到您过去问过一些问题，但尚未选择任何答案。非常感谢。它起作用了。我不知道我怎么会错过返回声明。不过我还有一个问题。在上述代码中，对zfeature执行集群的最佳方法是什么，这是一种张量运算。我得到了这个错误值error：当我将zfeature传递给kmeans时，设置一个带有序列的数组元素。这是一个相当大的问题。使用代码，您需要查询图形，然后应用kmeans和集群SKOPs。如果你没有完全致力于你的布局，那么这个网站有一个与TF集群的好例子。我为完整性创建了一个新的答案。

import tensorflow as tf
from random import choice, shuffle
from numpy import array


def TFKMeansCluster(vectors, noofclusters):
    """
    K-Means Clustering using TensorFlow.
    'vectors' should be a n*k 2-D NumPy array, where n is the number
    of vectors of dimensionality k.
    'noofclusters' should be an integer.
    """

    noofclusters = int(noofclusters)
    assert noofclusters < len(vectors)

    #Find out the dimensionality
    dim = len(vectors[0])

    #Will help select random centroids from among the available vectors
    vector_indices = list(range(len(vectors)))
    shuffle(vector_indices)

    #GRAPH OF COMPUTATION
    #We initialize a new graph and set it as the default during each run
    #of this algorithm. This ensures that as this function is called
    #multiple times, the default graph doesn't keep getting crowded with
    #unused ops and Variables from previous function calls.

    graph = tf.Graph()

    with graph.as_default():

        #SESSION OF COMPUTATION

        sess = tf.Session()

        ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

        ##First lets ensure we have a Variable vector for each centroid,
        ##initialized to one of the vectors from the available data points
        centroids = [tf.Variable((vectors[vector_indices[i]]))
                     for i in range(noofclusters)]
        ##These nodes will assign the centroid Variables the appropriate
        ##values
        centroid_value = tf.placeholder("float64", [dim])
        cent_assigns = []
        for centroid in centroids:
            cent_assigns.append(tf.assign(centroid, centroid_value))

        ##Variables for cluster assignments of individual vectors(initialized
        ##to 0 at first)
        assignments = [tf.Variable(0) for i in range(len(vectors))]
        ##These nodes will assign an assignment Variable the appropriate
        ##value
        assignment_value = tf.placeholder("int32")
        cluster_assigns = []
        for assignment in assignments:
            cluster_assigns.append(tf.assign(assignment,
                                             assignment_value))

        ##Now lets construct the node that will compute the mean
        #The placeholder for the input
        mean_input = tf.placeholder("float", [None, dim])
        #The Node/op takes the input and computes a mean along the 0th
        #dimension, i.e. the list of input vectors
        mean_op = tf.reduce_mean(mean_input, 0)

        ##Node for computing Euclidean distances
        #Placeholders for input
        v1 = tf.placeholder("float", [dim])
        v2 = tf.placeholder("float", [dim])
        euclid_dist = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(
            v1, v2), 2)))

        ##This node will figure out which cluster to assign a vector to,
        ##based on Euclidean distances of the vector from the centroids.
        #Placeholder for input
        centroid_distances = tf.placeholder("float", [noofclusters])
        cluster_assignment = tf.argmin(centroid_distances, 0)

        ##INITIALIZING STATE VARIABLES

        ##This will help initialization of all Variables defined with respect
        ##to the graph. The Variable-initializer should be defined after
        ##all the Variables have been constructed, so that each of them
        ##will be included in the initialization.
        init_op = tf.initialize_all_variables()

        #Initialize all variables
        sess.run(init_op)

        ##CLUSTERING ITERATIONS

        #Now perform the Expectation-Maximization steps of K-Means clustering
        #iterations. To keep things simple, we will only do a set number of
        #iterations, instead of using a Stopping Criterion.
        noofiterations = 100
        for iteration_n in range(noofiterations):

            ##EXPECTATION STEP
            ##Based on the centroid locations till last iteration, compute
            ##the _expected_ centroid assignments.
            #Iterate over each vector
            for vector_n in range(len(vectors)):
                vect = vectors[vector_n]
                #Compute Euclidean distance between this vector and each
                #centroid. Remember that this list cannot be named
                #'centroid_distances', since that is the input to the
                #cluster assignment node.
                distances = [sess.run(euclid_dist, feed_dict={
                    v1: vect, v2: sess.run(centroid)})
                             for centroid in centroids]
                #Now use the cluster assignment node, with the distances
                #as the input
                assignment = sess.run(cluster_assignment, feed_dict = {
                    centroid_distances: distances})
                #Now assign the value to the appropriate state variable
                sess.run(cluster_assigns[vector_n], feed_dict={
                    assignment_value: assignment})

            ##MAXIMIZATION STEP
            #Based on the expected state computed from the Expectation Step,
            #compute the locations of the centroids so as to maximize the
            #overall objective of minimizing within-cluster Sum-of-Squares
            for cluster_n in range(noofclusters):
                #Collect all the vectors assigned to this cluster
                assigned_vects = [vectors[i] for i in range(len(vectors))
                                  if sess.run(assignments[i]) == cluster_n]
                #Compute new centroid location
                new_location = sess.run(mean_op, feed_dict={
                    mean_input: array(assigned_vects)})
                #Assign value to appropriate variable
                sess.run(cent_assigns[cluster_n], feed_dict={
                    centroid_value: new_location})

        #Return centroids and assignments
        centroids = sess.run(centroids)
        assignments = sess.run(assignments)
        return centroids, assignments