Tensorflow实践：用神经网络训练分类器

正文共5873个字，3张图，预计阅读时间15分钟。

任务：

使用tensorflow训练一个神经网络作为分类器，分类的数据点如下：

螺旋形数据点

原理

数据点一共有三个类别，而且是螺旋形交织在一起，显然是线性不可分的，需要一个非线性的分类器。这里选择神经网络。

输入的数据点是二维的，因此每个点只有x,y坐标这个原始特征。这里设计的神经网络有两个隐藏层，每层有50个神经元，足够抓住数据点的高维特征（实际上每层10个都够用了）。最后输出层是一个逻辑回归，根据隐藏层计算出的50个特征来预测数据点的分类（红、黄、蓝）。

一般训练数据多的话，应该用随机梯度下降来训练神经网络，这里训练数据较少（300），就直接批量梯度下降了。

# 导入包、初始化

import numpy as np

import matplotlib.pyplot as plt

import tensorflow as tf %matplotlib inline plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots

plt.rcParams['image.interpolation'] = 'nearest'

plt.rcParams['image.cmap'] = 'gray

'# 生成螺旋形的线形不可分数据点

np.random.seed(0) N = 100 # 每个类的数据个数

D = 2 # 输入维度

K = 3 # 类的个数

X = np.zeros((N*K,D)) num_train_examples = X.shape[0] y = np.zeros(N*K, dtype='uint8')

for j in xrange(K): ix = range(N*j,N*(j+1)) r = np.linspace(0.0,1,N) # radius t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta X[ix] = np.c_[r*np.sin(t), r*np.cos(t)] y[ix] = j fig = plt.figure() plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral) plt.xlim([-1,1]) plt.ylim([-1,1])

螺旋形数据点

打印输出输入X和label的shape

num_label = 3

labels = (np.arange(num_label) == y[:,None]).astype(np.float32) labels.shape

(300, 3)

X.shape

(300, 2)

用tensorflow构建神经网络

import math N = 100 # 每个类的数据个数

D = 2 # 输入维度

num_label = 3 # 类的个数

num_data = N * num_label hidden_size_1 = 50

hidden_size_2 = 50

beta = 0.001 # L2 正则化系数

learning_rate = 0.1 # 学习速率

labels = (np.arange(num_label) == y[:,None]).astype(np.float32) graph = tf.Graph()

with graph.as_default(): x = tf.constant(X.astype(np.float32)) tf_labels = tf.constant(labels) # 隐藏层1 hidden_layer_weights_1 = tf.Variable( tf.truncated_normal([D, hidden_size_1], stddev=math.sqrt(2.0/num_data))) hidden_layer_bias_1 = tf.Variable(tf.zeros([hidden_size_1])) # 隐藏层2 hidden_layer_weights_2 = tf.Variable( tf.truncated_normal([hidden_size_1, hidden_size_2], stddev=math.sqrt(2.0/hidden_size_1))) hidden_layer_bias_2 = tf.Variable(tf.zeros([hidden_size_2])) # 输出层 out_weights = tf.Variable( tf.truncated_normal([hidden_size_2, num_label], stddev=math.sqrt(2.0/hidden_size_2))) out_bias = tf.Variable(tf.zeros([num_label])) z1 = tf.matmul(x, hidden_layer_weights_1) + hidden_layer_bias_1 h1 = tf.nn.relu(z1) z2 = tf.matmul(h1, hidden_layer_weights_2) + hidden_layer_bias_2 h2 = tf.nn.relu(z2) logits = tf.matmul(h2, out_weights) + out_bias # L2正则化 regularization = tf.nn.l2_loss(hidden_layer_weights_1) + tf.nn.l2_loss(hidden_layer_weights_2) + tf.nn.l2_loss(out_weights) loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=tf_labels, logits=logits) + beta * regularization) optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) train_prediction = tf.nn.softmax(logits) weights = [hidden_layer_weights_1, hidden_layer_bias_1, hidden_layer_weights_2, hidden_layer_bias_2, out_weights, out_bias]

上一步相当于搭建了神经网络的骨架，现在需要训练。每1000步训练，打印交叉熵损失和正确率。

num_steps = 50000

def accuracy(predictions, labels): return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])def relu(x): return np.maximum(0,x)

with tf.Session(graph=graph) as session: tf.global_variables_initializer().run() print('Initialized')

for step in range(num_steps): _, l, predictions = session.run([optimizer, loss, train_prediction]) if (step % 1000 == 0): print('Loss at step %d: %f' % (step, l)) print('Training accuracy: %.1f%%' % accuracy( predictions, labels)) w1, b1, w2, b2, w3, b3 = weights

# 显示分类器 h = 0.02 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = np.dot(relu(np.dot(relu(np.dot(np.c_[xx.ravel(), yy.ravel()], w1.eval()) + b1.eval()), w2.eval()) + b2.eval()), w3.eval()) + b3.eval() Z = np.argmax(Z, axis=1) Z = Z.reshape(xx.shape) fig = plt.figure() plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8) plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral) plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max())

Initialized

Loss at step 0: 1.132545

Training accuracy: 43.7%

Loss at step 1000: 0.257016

Training accuracy: 94.0%

Loss at step 2000: 0.165511

Training accuracy: 98.0%

Loss at step 3000: 0.149266

Training accuracy: 99.0%

Loss at step 4000: 0.142311

Training accuracy: 99.3%

Loss at step 5000: 0.137762

Training accuracy: 99.3%

Loss at step 6000: 0.134356

Training accuracy: 99.3%

Loss at step 7000: 0.131588

Training accuracy: 99.3%

Loss at step 8000: 0.129299

Training accuracy: 99.3%

Loss at step 9000: 0.127340

Training accuracy: 99.3%

Loss at step 10000: 0.125686

Training accuracy: 99.3%

Loss at step 11000: 0.124293

Training accuracy: 99.3%

Loss at step 12000: 0.123130

Training accuracy: 99.3%

Loss at step 13000: 0.122149

Training accuracy: 99.3%

Loss at step 14000: 0.121309

Training accuracy: 99.3%

Loss at step 15000: 0.120542

Training accuracy: 99.3%

Loss at step 16000: 0.119895

Training accuracy: 99.3%

Loss at step 17000: 0.119335

Training accuracy: 99.3%

Loss at step 18000: 0.118836

Training accuracy: 99.3%

Loss at step 19000: 0.118376

Training accuracy: 99.3%

Loss at step 20000: 0.117974

Training accuracy: 99.3%

Loss at step 21000: 0.117601

Training accuracy: 99.3%

Loss at step 22000: 0.117253

Training accuracy: 99.3%

Loss at step 23000: 0.116887

Training accuracy: 99.3%

Loss at step 24000: 0.116561

Training accuracy: 99.3%

Loss at step 25000: 0.116265

Training accuracy: 99.3%

Loss at step 26000: 0.115995

Training accuracy: 99.3%

Loss at step 27000: 0.115750

Training accuracy: 99.3%

Loss at step 28000: 0.115521

Training accuracy: 99.3%

Loss at step 29000: 0.115310

Training accuracy: 99.3%

Loss at step 30000: 0.115111

Training accuracy: 99.3%

Loss at step 31000: 0.114922

Training accuracy: 99.3%

Loss at step 32000: 0.114743

Training accuracy: 99.3%

Loss at step 33000: 0.114567

Training accuracy: 99.3%

Loss at step 34000: 0.114401

Training accuracy: 99.3%

Loss at step 35000: 0.114242

Training accuracy: 99.3%

Loss at step 36000: 0.114086

Training accuracy: 99.3%

Loss at step 37000: 0.113933

Training accuracy: 99.3%

Loss at step 38000: 0.113785

Training accuracy: 99.3%

Loss at step 39000: 0.113644

Training accuracy: 99.3%

Loss at step 40000: 0.113504

Training accuracy: 99.3%

Loss at step 41000: 0.113366

Training accuracy: 99.3%

Loss at step 42000: 0.113229

Training accuracy: 99.3%

Loss at step 43000: 0.113096

Training accuracy: 99.3%

Loss at step 44000: 0.112966

Training accuracy: 99.3%

Loss at step 45000: 0.112838

Training accuracy: 99.3%

Loss at step 46000: 0.112711

Training accuracy: 99.3%

Loss at step 47000: 0.112590

Training accuracy: 99.3%

Loss at step 48000: 0.112472

Training accuracy: 99.3%

Loss at step 49000: 0.112358

Training accuracy: 99.3%

分类器.png

原文链接：https://www.jianshu.com/p/25a709c70ae3

查阅更为简洁方便的分类文章以及最新的课程、产品信息，请移步至全新呈现的“LeadAI学院官网”：

www.leadai.org

请关注人工智能LeadAI公众号，查看更多专业文章

Tensorflow实践：用神经网络训练分类器相关推荐

关于Tensorflow用于深度神经网络训练中的一些问题
小弟刚接触tensorflow,之前在一篇文章中看到一个用于DNN神经网络预测,想尝试对该代码进行一点修改.原文:https://blog.csdn.net/xxzhangx/article/deta ...
建立简单的卷积神经网络训练分类器，识别九宫格、四宫格等图片
本文想实现一个分类器模型,该模型能够识别出九宫格图片并将图片进行切割,首先我要构造训练集,也就是自己生成一些九宫格图片. 其中r=3表示将其切割为3行,c=3表示将其切割为3列.你也可以生成4宫格图片 ...
【神经网络与深度学习-TensorFlow实践】-中国大学MOOC课程（十二）（人工神经网络（1）））
[神经网络与深度学习-TensorFlow实践]-中国大学MOOC课程(十二)(人工神经网络(1))) 12 人工神经网络(1) 12.1 神经元与感知机 12.1.1 感知机 12.1.2 Delt ...
【神经网络与深度学习-TensorFlow实践】-中国大学MOOC课程（十四）（卷积神经网络））
[神经网络与深度学习-TensorFlow实践]-中国大学MOOC课程(十四)(卷积神经网络)) 14 卷积神经网络 14.1 深度学习基础 14.1.1 深度学习的基本思想 14.1.2 深度学习三 ...
训练softmax分类器实例_知识蒸馏：如何用一个神经网络训练另一个神经网络
作者:Tivadar Danka 编译:ronghuaiyang 原文链接知识蒸馏:如何用一个神经网络训练另一个神经网络mp.weixin.qq.com 导读知识蒸馏的简单介绍,让大家了解知识蒸 ...
【神经网络与深度学习-TensorFlow实践】-中国大学MOOC课程（八）（TensorFlow基础））
[神经网络与深度学习-TensorFlow实践]-中国大学MOOC课程(八)(TensorFlow基础)) 8 TensorFlow基础 8.1 TensorFlow2.0特性 8.1.1 Tenso ...
美团图神经网络训练框架的实践和探索
美团搜索与NLP团队在图神经网络的长期落地实践中,基于业务实际场景,自主设计研发了图神经网络框架Tulong,以及配套的图学习平台,提升了模型的规模和迭代效率.本文介绍了模型归纳抽象.基本框架.性能优 ...
python神经网络原理pdf_《深度学习原理与 TensorFlow实践》高清完整PDF版下载
1.封面介绍 2.出版时间 2019年7月 3.推荐理由本书介绍了深度学习原理与TensorFlow实践.着重讲述了当前学术界和工业界的深度学习核心知识:机器学习概论.神经网络.深度学习.着重讲述了 ...
CNN卷积神经网络—LeNet原理以及tensorflow实现mnist手写体训练
CNN卷积神经网络-LeNet原理以及tensorflow实现minst手写体训练 1. LeNet原理 2.tensorflow实现Mnist手写体识别 1.安装tensorflow 2.代码实现手 ...

Tensorflow实践：用神经网络训练分类器

任务：

Tensorflow实践：用神经网络训练分类器相关推荐

最新文章

热门文章