利用卷积神经网络模型预测mnist数据集的准确率

利用卷积神经网络模型预测mnist数据集的准确率

本例程采用两个卷积层

本例程中卷积神经网络模型的构建基本分为以下几步：

输入数据，定义相关变量和参数

初始化权值和偏置值

定义卷积层和池化层函数

构建第一个卷积层和第二个卷积层

构建第一个全连接层和第二个全连接层

采用交叉熵代价函数

使用AdamOptimizer进行优化

求测试准确率

定义Session会话，开始训练模型

在定义不同变量和函数时，建议采用tf.name_scope来使代码模块化。

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

# 把mnist数据集读取内存.这里注意one_hot=True这个参数.

# one_hot表示用非零即1的数组保存图片表示的数值.比如一个图片上写的是0,

# 内存中不是直接存一个0,而是存一个数组[1,0,0,0,0,0,0,0,0,0].

# 一个图片上面写的是1,那个保存的就是[0,1,0,0,0,0,0,0,0,0]

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# 每个批次的大小

batch_size = 100

# 计算一共有多少个批次

n_batch = mnist.train.num_examples // batch_size

# 参数概要

def variable_summaries(var):

with tf.name_scope('summaries'):

mean = tf.reduce_mean(var)

tf.summary.scalar('mean', mean) # 平均值

with tf.name_scope('stddev'):

stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))

tf.summary.scalar('stddev', stddev) # 标准差

tf.summary.scalar('max', tf.reduce_max(var)) # 最大值

tf.summary.scalar('min', tf.reduce_min(var)) # 最小值

tf.summary.histogram('histogram', var) # 直方图

# 初始化权值

def weight_variable(shape, name):

initial = tf.truncated_normal(shape, stddev=0.1) # 生成一个截断的正态分布

return tf.Variable(initial, name=name)

# 初始化偏置

def bias_variable(shape, name):

initial = tf.constant(0.1, shape=shape)

return tf.Variable(initial, name=name)

# 卷积层

def conv2D(x, W):

# x input tensor of shape `[batch, in_height, in_width, in_channels]`

# W filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]

# `strides[0] = strides[3] = 1`. strides[1]代表x方向的步长，strides[2]代表y方向的步长

# padding: A `string` from: `"SAME", "VALID"`

return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

# 池化层

def max_pool_2x2(x):

# ksize [1,x,y,1]

return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 命名空间

with tf.name_scope('input'):

# 定义两个placeholder

x = tf.placeholder(tf.float32, [None, 784], name='x-input')

y = tf.placeholder(tf.float32, [None, 10], name='y-input')

with tf.name_scope('x_image'):

# 改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]`

x_image = tf.reshape(x, [-1, 28, 28, 1], name='x_image')

with tf.name_scope('Conv1'):

# 初始化第一个卷积层的权值和偏置

with tf.name_scope('W_conv1'):

W_conv1 = weight_variable([5, 5, 1, 32], name='W_conv1') # 5*5的采样窗口，32个卷积核从1个平面抽取特征

variable_summaries(W_conv1)

with tf.name_scope('b_conv1'):

b_conv1 = bias_variable([32], name='b_conv1') # 每一个卷积核一个偏置值

variable_summaries(b_conv1)

# 把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数

with tf.name_scope('conv2d_1'):

conv2d_1 = conv2D(x_image, W_conv1) + b_conv1

with tf.name_scope('relu'):

h_conv1 = tf.nn.relu(conv2d_1)

with tf.name_scope('h_pool1'):

h_pool1 = max_pool_2x2(h_conv1) # 进行max-pooling

with tf.name_scope('Conv2'):

# 初始化第二个卷积层的权值和偏置

with tf.name_scope('W_conv2'):

W_conv2 = weight_variable([5, 5, 32, 64], name='W_conv2') # 5*5的采样窗口，64个卷积核从32个平面抽取特征

with tf.name_scope('b_conv2'):

b_conv2 = bias_variable([64], name='b_conv2') # 每一个卷积核一个偏置值

# 把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数

with tf.name_scope('conv2d_2'):

conv2d_2 = conv2D(h_pool1, W_conv2) + b_conv2

with tf.name_scope('relu'):

h_conv2 = tf.nn.relu(conv2d_2)

with tf.name_scope('h_pool2'):

h_pool2 = max_pool_2x2(h_conv2) # 进行max-pooling

# 28*28的图片第一次卷积后还是28*28，第一次池化后变为14*14

# 第二次卷积后为14*14，第二次池化后变为了7*7

# 进过上面操作后得到64张7*7的平面

with tf.name_scope('fc1'):

# 初始化第一个全连接层的权值

with tf.name_scope('W_fc1'):

W_fc1 = weight_variable([7 * 7 * 64, 1024], name='W_fc1') # 上一场有7*7*64个神经元，全连接层有1024个神经元

with tf.name_scope('b_fc1'):

b_fc1 = bias_variable([1024], name='b_fc1') # 1024个节点

# 把池化层2的输出扁平化为1维

with tf.name_scope('h_pool2_flat'):

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64], name='h_pool2_flat')

# 求第一个全连接层的输出

with tf.name_scope('wx_plus_b1'):

wx_plus_b1 = tf.matmul(h_pool2_flat, W_fc1) + b_fc1

with tf.name_scope('relu'):

h_fc1 = tf.nn.relu(wx_plus_b1)

# keep_prob用来表示神经元的输出概率

with tf.name_scope('keep_prob'):

keep_prob = tf.placeholder(tf.float32, name='keep_prob')

with tf.name_scope('h_fc1_drop'):

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob, name='h_fc1_drop')

with tf.name_scope('fc2'):

# 初始化第二个全连接层

with tf.name_scope('W_fc2'):

W_fc2 = weight_variable([1024, 10], name='W_fc2')

with tf.name_scope('b_fc2'):

b_fc2 = bias_variable([10], name='b_fc2')

with tf.name_scope('wx_plus_b2'):

wx_plus_b2 = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

with tf.name_scope('softmax'):

# 计算输出

prediction = tf.nn.softmax(wx_plus_b2)

# 交叉熵代价函数

with tf.name_scope('cross_entropy'):

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction),

name='cross_entropy')

tf.summary.scalar('cross_entropy', cross_entropy)

# 使用AdamOptimizer进行优化

with tf.name_scope('train'):

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# 求准确率

with tf.name_scope('accuracy'):

with tf.name_scope('correct_prediction'):

# 结果存放在一个布尔列表中

correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) # argmax返回一维张量中最大的值所在的位置

with tf.name_scope('accuracy'):

# 求准确率

# reduce_mean求均值

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

tf.summary.scalar('accuracy', accuracy)

# 合并所有的summary

merged = tf.summary.merge_all()

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

train_writer = tf.summary.FileWriter('logs/train', sess.graph)

test_writer = tf.summary.FileWriter('logs/test', sess.graph)

for i in range(1001):

# 训练模型

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

sess.run(train_step, feed_dict=)

# 记录训练集计算的参数

summary = sess.run(merged, feed_dict=)

train_writer.add_summary(summary, i)

# 记录测试集计算的参数

batch_xs, batch_ys = mnist.test.next_batch(batch_size)

summary = sess.run(merged, feed_dict=)

test_writer.add_summary(summary, i)

if i % 100 == 0:

test_acc = sess.run(accuracy, feed_dict=)

train_acc = sess.run(accuracy, feed_dict=)

print("Iter " + str(i) + ", Testing Accuracy= " + str(test_acc) + ", Training Accuracy= " + str(train_acc))