一看就懂的Tensorflow实战（K-Means模型）

AI异构

发布于 2020-07-29 11:22:41

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发布于 2020-07-29 11:22:41

文章被收录于专栏：AI异构AI异构

K-Means算法简介

K-MEANS算法是输入聚类个数k，以及包含 n个数据对象的数据库，输出满足方差最小标准k个聚类的一种算法。属于一种经典的无监督学习算法。示意图如下所示：

K-Means算法示意图

k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。

基本步骤：

（1）从 n个数据对象任意选择 k 个对象作为初始聚类中心；

（2）根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新对相应对象进行划分；

（3）重新计算每个（有变化）聚类的均值（中心对象）；

（4）计算标准测度函数，当满足一定条件，如函数收敛时，则算法终止；如果条件不满足则回到步骤（2）。

TensorFlow的K-Means实现

from __future__ import print_function

import numpy as np
import tensorflow as tf
from tensorflow.contrib.factorization import KMeans#导入KMeans函数

# Ignore all GPUs, tf random forest does not benefit from it.
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""

导入数据集

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./data/", one_hot=True)
full_data_x = mnist.train.images

Extracting ./data/train-images-idx3-ubyte.gz
Extracting ./data/train-labels-idx1-ubyte.gz
Extracting ./data/t10k-images-idx3-ubyte.gz
Extracting ./data/t10k-labels-idx1-ubyte.gz

设置参数

# Parameters
num_steps = 50 # Total steps to train
batch_size = 1024 # The number of samples per batch
k = 25 # The number of clusters
num_classes = 10 # The 10 digits
num_features = 784 # Each image is 28x28 pixels

# Input images
X = tf.placeholder(tf.float32, shape=[None, num_features])
# Labels (for assigning a label to a centroid and testing)
Y = tf.placeholder(tf.float32, shape=[None, num_classes])

# K-Means Parameters
# 距离度量的方式采用余弦距离（余弦相似度）
kmeans = KMeans(inputs=X, num_clusters=k, distance_metric='cosine',
                use_mini_batch=True)

构建K-means图模型

# Build KMeans graph
(all_scores, cluster_idx, scores, cluster_centers_initialized,init_op,train_op) = kmeans.training_graph()
cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple
avg_distance = tf.reduce_mean(scores)

# Initialize the variables (i.e. assign their default value)
init_vars = tf.global_variables_initializer()

训练模型

# Start TensorFlow session
sess = tf.Session()

# Run the initializer
sess.run(init_vars, feed_dict={X: full_data_x})
sess.run(init_op, feed_dict={X: full_data_x})

# Training
for i in range(1, num_steps + 1):
    _, d, idx = sess.run([train_op, avg_distance, cluster_idx],
                         feed_dict={X: full_data_x})
    if i % 10 == 0 or i == 1:
        print("Step %i, Avg Distance: %f" % (i, d))

Step 1, Avg Distance: 0.341471
Step 10, Avg Distance: 0.221609
Step 20, Avg Distance: 0.220328
Step 30, Avg Distance: 0.219776
Step 40, Avg Distance: 0.219419
Step 50, Avg Distance: 0.219154

测试

# Assign a label to each centroid
# Count total number of labels per centroid, using the label of each training
# sample to their closest centroid (given by 'idx')
counts = np.zeros(shape=(k, num_classes))
for i in range(len(idx)):
    counts[idx[i]] += mnist.train.labels[i]
# Assign the most frequent label to the centroid
labels_map = [np.argmax(c) for c in counts]
labels_map = tf.convert_to_tensor(labels_map)

# Evaluation ops
# Lookup: centroid_id -> label
cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx)
# Compute accuracy
correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Test Model
test_x, test_y = mnist.test.images, mnist.test.labels
print("Test Accuracy:", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y}))