Need Help to feed data To Tensor

input_data.csv

kmeans.py

from __future__ import print_function

import numpy as np
import pandas as pd
from sklearn.cluster import KMeans, DBSCAN, MiniBatchKMeans
import tensorflow as tf

MATRIX_SIZE = 110#features 110
DATA_SET_URL = "./movie_data/input_data.csv"
CLUSTER = 50
BATCH_SIZE = 1000


def read_data(): 
  Xtr = []
  Xtr = np.array(pd.read_csv(DATA_SET_URL, header=None, skiprows=1),dtype="float")
  Xtr = Xtr.astype("float")
  return Xtr#Xtr#tf.cast(Xtr, tf.float32) #tf.constant(Xtr)

def placeholder_inputs():
  points = tf.placeholder(tf.float32, [None,110])
  return points

def fill_feed_dict(data_set,points):
  #input_feed  = data_set.next_batch(batch_size)
  feed_dict = {
      points : data_set
  }
  return feed_dict


def kmeanTF(points):
  centroids = tf.Variable(tf.slice(tf.random_shuffle(points), [0, 0], [CLUSTER, -1]))

  points_expanded = tf.expand_dims(points, 0)
  centroids_expanded = tf.expand_dims(centroids, 1)

  distances = tf.reduce_sum(tf.square(tf.sub(points_expanded, centroids_expanded)), 2)
  assignments = tf.argmin(distances, 0)

  means = []
  for c in xrange(CLUSTER):
      means.append(tf.reduce_mean(
        tf.gather(points, 
                  tf.reshape(
                    tf.where(
                      tf.equal(assignments, c)
                    ),[1,-1])
                 ),reduction_indices=[1]))

  new_centroids = tf.concat(0, means)

  update_centroids = tf.assign(centroids, new_centroids)
  return update_centroids, centroids, assignments

def run_training():
    data_set = read_data();
    points = placeholder_inputs();
    feed_dict = fill_feed_dict(data_set,points)
    assignment_values,centroid_value= kmeanTF(points);
    init = tf.initialize_all_variables()
    # Create a session for running Ops on the Graph.
    sess = tf.InteractiveSession()

    # Run the Op to initialize the variables.
    sess.run(init)

    for step in xrange(5):
        [assignment_values] = sess.run([assignments],feed_dict)
        print("assignment_values" + "\n",assignment_values)

 
run_training()