CNN

MNISIT卷积网络代码

#!/usr/bin/python2.7
# -*- coding: UTF-8 -*-


import tensorflow as tf
from tensorflow.examples.tutorials.mnist  import input_data



# 载入数据
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)
# 设置批次的大小
batch_size=100
# 计算一共有多少的批次
n_batch=mnist.train.num_examples // batch_size


# 初始化权值
def weight_variable(shape):

    initial=tf.truncated_normal(shape,stddev=0.1)  #生产一个截断的正态分布
    return tf.Variable(initial)

# 初始化偏执值
def bias_variable(shape):
    initial=tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

# 卷积层
def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

#  x是[batch,in_height,in_width,in_channels]: n_height,in_width:图片的长和宽，in_channels：通道数(黑白=1，彩色=3)
#  W：卷积核：[filter_height,filter_width,in_channels,out_channels]
#  strides:步长：strides[0]=strides[3]=0  , stride[1]是x方向的步长，strides[2]是y方向的步长
#  padding:‘SAME'

# 池化层：max_polling
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
# ksize：[1,x,y,1]: 窗口的大小：2*2

# 定义placeholder
x=tf.placeholder(tf.float32,[None,784])  #28*28
y=tf.placeholder(tf.float32,[None,10])

# 将x的格式改变为4D向量: [batcj,in_height,in_width,in_channels]
x_image=tf.reshape(x,[-1,28,28,1])

# 初始化第一个卷积的权值和偏置
W_conv1=weight_variable([5,5,1,32]) # 5*5的采样窗口，32(输出的通道数，输出32个卷积核)个卷积核从1个平面抽取特征（黑白所以是1）
b_conv1=bias_variable([32]) #每个卷积核是1个偏执值

#　对ｘ_image和权值卷积操作，然后加上偏执值，然后应用relu激活函数
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h_pool1=max_pool_2x2(h_conv1)


##初始化第二个卷积层的权值和偏置
W_conv2=weight_variable([5,5,32,64]) # 5*5的采样窗口，64个卷积从32个平面抽取特征
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
h_pool2=max_pool_2x2(h_conv2)

#28*28的图片第一次卷积后还是２８＊２８，第一次池化后是１４＊１４
#第二次卷积后是１４＊１４，第二次池化后是７＊７
#　经过上面的操作后得到６４张７＊７的平面

#初始化第一个全连接层
W_fc1=weight_variable([7*7*64,1024])  #上层是7*7*64的神经元，全连接层是１０２４神经元
b_fc1=bias_variable([1024])

#将池化层２的输出扁平化为１维
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])

# 求第一个全连接层的输出
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)

# keep_prob表示神经元的输出概况
keep_prob=tf.placeholder(tf.float32)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)

#初始化第二个全连接层
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])

# 计算输出
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

# 交叉商代价函数
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
#使用admOptimizer进行优化
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#　结果存放在布尔列表
correct_prediction=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1)) #argmax返回１维张量中最大值所在的位置

#求准确率
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(21):
        for batch in range(n_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.7})

        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
        print("Iter "+str(epoch)+" , Testing Accuracy= "+ str(acc))

可视化

#!/usr/bin/python2.7
# -*- coding: UTF-8 -*-

#!/usr/bin/python2.7
# -*- coding: UTF-8 -*-
#!/usr/bin/python2.7
# -*- coding: UTF-8 -*-


import tensorflow as tf
from tensorflow.examples.tutorials.mnist  import input_data



# 载入数据
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)
# 设置批次的大小
batch_size=100
# 计算一共有多少的批次
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):

    initial=tf.truncated_normal(shape,stddev=0.1)  #生产一个截断的正态分布
    return tf.Variable(initial)

# 初始化偏执值
def bias_variable(shape):
    initial=tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

# 卷积层
def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

#  x是[batch,in_height,in_width,in_channels]: n_height,in_width:图片的长和宽，in_channels：通道数(黑白=1，彩色=3)
#  W：卷积核：[filter_height,filter_width,in_channels,out_channels]
#  strides:步长：strides[0]=strides[3]=0  , stride[1]是x方向的步长，strides[2]是y方向的步长
#  padding:‘SAME'

# 池化层：max_polling
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
# ksize：[1,x,y,1]: 窗口的大小：2*2


##################################################
# 命名空间

#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
    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

    # 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
        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={x: batch_xs, y: batch_ys, keep_prob: 0.5})
        # 记录训练集计算的参数
        summary = sess.run(merged, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.0})
        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={x: batch_xs, y: batch_ys, keep_prob: 1.0})
        test_writer.add_summary(summary, i)

        if i % 100 == 0:
            test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})
            train_acc = sess.run(accuracy, feed_dict={x: mnist.train.images[:10000], y: mnist.train.labels[:10000],
                                                      keep_prob: 1.0})
            print ("Iter " + str(i) + ", Testing Accuracy= " + str(test_acc) + ", Training Accuracy= " + str(train_acc))