深度学习与TensorFlow实战(九)卷积神经网络—Lenet神经网络

Lenet神经网络是YannLeCun等人在1998年提出的,该神经网络充分考虑图像的相关性。
Lenet神经网络结构为:
1),输入为32X32X1的图片大小,为单通道;
2),进行卷积,卷积核大小为5X5X1,个数为6,步长为1,非0填充;
3)将卷积结果通过非线性激活函数;
4)进行池化,池化大小为2X2,步长为1,全0填充;
5)进行卷积,卷积核大小为5X5X6,个数16,步长1,非全0填充;
6)将卷积结果通过非线性激活函数;
7)进行池化,池化大小为2X2,步长1,全0填充;
8)全连接层进行10分类。

Lenet神经网络的结构图及特征提取过程如下:
深度学习与TensorFlow实战(九)卷积神经网络—Lenet神经网络_第1张图片

根据Lenet神经网络的结构可得,Lenet具有以下特点:
1)卷积(conv),池化(ave-pooling),非线性激活函数(sigmoid)相互交替;
2)层与层之间稀疏连接,减少计算复杂度。

对Lenet进行微调整,使其适应Mnist数据集:
由于Mnist数据集图片大小为28X28X1的灰度图片,而Lenet神经网络的输入为32X32X1的大小,所以要进行微调;
①输入为28*28*1的图片大小,为单通道的输入;
②进行卷积,卷积核大小为5*5*1,个数为32,步长为1,全零填充模式;
③将卷积结果通过非线性激活函数;
④进行池化,池化大小为2*2,步长为2,全零填充模式;
⑤进行卷积,卷积核大小为5*5*32,个数为64,步长为1,全零填充模式;
⑥将卷积结果通过非线性激活函数;
⑦进行池化,池化大小为2*2,步长为2,全零填充模式;
⑧全连接层,进行10分类。

Lenet进行微调后的结构如下所示:
深度学习与TensorFlow实战(九)卷积神经网络—Lenet神经网络_第2张图片
Lenet神经网络在Mnist数据集上的实现,主要分为三个部分:前向传播过程(mnist_lenet5_forward.py)、反向传播过程(mnist_lenet5_backword.py)、测试过程(mnist_lenet5_test.py)。
第一,前向传播过程(mnist_lenet5_forward.py)实现对网络中参数和偏置的初始化、定义卷积结构和池化结构、定义前向传播过程。具体代码如下所示:

# coding:utf-8
import tensorflow as tf

# 每张图片分辨率为28*28
IMAGE_SIZE = 28
# Mnist数据集为灰度图,故输入图片通道数NUM_CHANNELS取值为1
NUM_CHANNELS = 1
# 第一层卷积核大小为5
CONV1_SIZE = 5
# 卷积核个数为32
CONV1_KERNEL_NUM = 32
# 第二层卷积核大小为5
CONV2_SIZE = 5
# 卷积核个数为64
CONV2_KERNEL_NUM = 64
# 全连接层第一层为 512 个神经元
FC_SIZE = 512
# 全连接层第二层为 10 个神经元
OUTPUT_NODE = 10


# 权重w计算
def get_weight(shape, regularizer):
    w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
    if regularizer != None: tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w


# 偏置b计算
def get_bias(shape):
    b = tf.Variable(tf.zeros(shape))
    return b


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


# 最大池化层计算
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


def forward(x, train, regularizer):
    # 实现第一层卷积
    conv1_w = get_weight([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_KERNEL_NUM], regularizer)
    conv1_b = get_bias([CONV1_KERNEL_NUM])
    conv1 = conv2d(x, conv1_w)
    # 非线性激活
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_b))
    # 最大池化
    pool1 = max_pool_2x2(relu1)

    # 实现第二层卷积
    conv2_w = get_weight([CONV2_SIZE, CONV2_SIZE, CONV1_KERNEL_NUM, CONV2_KERNEL_NUM], regularizer)
    conv2_b = get_bias([CONV2_KERNEL_NUM])
    conv2 = conv2d(pool1, conv2_w)
    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_b))
    pool2 = max_pool_2x2(relu2)

    # 获取一个张量的维度
    pool_shape = pool2.get_shape().as_list()
    # pool_shape[1] 为长 pool_shape[2] 为宽 pool_shape[3]为高
    nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
    # 得到矩阵被拉长后的长度,pool_shape[0]为batch值
    reshaped = tf.reshape(pool2, [pool_shape[0], nodes])

    # 实现第三层全连接层
    fc1_w = get_weight([nodes, FC_SIZE], regularizer)
    fc1_b = get_bias([FC_SIZE])
    fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
    # 如果是训练阶段,则对该层输出使用dropout
    if train: fc1 = tf.nn.dropout(fc1, 0.5)

    # 实现第四层全连接层
    fc2_w = get_weight([FC_SIZE, OUTPUT_NODE], regularizer)
    fc2_b = get_bias([OUTPUT_NODE])
    y = tf.matmul(fc1, fc2_w) + fc2_b
    return y

第二,反向传播过程(mnist_lenet5_backward.py),完成训练神经网络的参数。具体代码如下所示:

# coding:utf-8
import tensorflow as tf
import mnist_lenet5_forward
import numpy as np
import os
from tensorflow.examples.tutorials.mnist import input_data

# batch的数量
BATCH_SIZE = 100
# 初始学习率
LEARNING_RATE_BASE = 0.005
# 学习率衰减率
LEARNING_RATE_DECAY = 0.99
# 正则化
REGULARIZER = 0.0001
# 滑动平均衰减率
MOVING_AVERAGE_DECAY = 0.99
# 模型保存路径
MODEL_SAVE_PATH = "./model/"
# 模型名称
MODEL_NAME = "mnist_model"


def backward(mnist):
    # 卷积层输入为四阶张量
    # 第一阶表示每轮喂入的图片数量,第二阶和第三阶分别表示图片的行分辨率和列分辨率,第四阶表示通道数
    x = tf.placeholder(tf.float32, [
        BATCH_SIZE,
        mnist_lenet5_forward.IMAGE_SIZE,
        mnist_lenet5_forward.IMAGE_SIZE,
        mnist_lenet5_forward.NUM_CHANNELS
    ])
    y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE])
    y = mnist_lenet5_forward.forward(x, True, REGULARIZER)
    # 声明一个全局计数器
    global_step = tf.Variable(0, trainable=False)
    # 对网络最后一层的输出y做softmax,求取输出属于某一类的概率
    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
    # 向量求均值
    cem = tf.reduce_mean(ce)
    # 正则化的损失值
    loss = cem + tf.add_n(tf.get_collection('losses'))
    # 指数衰减学习率
    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE,
        LEARNING_RATE_DECAY,
        staircase=True
    )
    # 梯度下降算法优化器
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
    # 采用滑动平均的方法更新参数
    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    ema_op = ema.apply(tf.trainable_variables())
    # 将train_step和ema两个训练参数绑定train_op上
    with tf.control_dependencies([train_step, ema_op]):
        train_op = tf.no_op(name='train')

    # 实例化一个保存和恢复变量的saver
    saver = tf.train.Saver()

    with tf.Session() as sess:
        init_op=tf.global_variables_initializer()
        sess.run(init_op)
        #通过checkpoint文件定位到最新保存的模型,若文件存在,则加载最新模型
        ckpt=tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
        if ckpt and ckpt.model_checkpoint_path:
           saver.restore(sess,ckpt.model_checkpoint_path)

        for i in range(5000):
            #读取一个batch数据,将输入数据xs转成网络输入相同形状的矩阵
            xs,ys=mnist.train.next_batch(BATCH_SIZE)
            reshaped_xs=np.reshape(xs,(
                BATCH_SIZE,
                mnist_lenet5_forward.IMAGE_SIZE,
                mnist_lenet5_forward.IMAGE_SIZE,
                mnist_lenet5_forward.NUM_CHANNELS
            ))
            _,loss_value,step=sess.run([train_step,loss,global_step],feed_dict={x:reshaped_xs,y_:ys})
            if i%100==0:
                print("after %d training step,loss is %g"%(step,loss_value))
                saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step)

def main():
    mnist=input_data.read_data_sets("./data/",one_hot=True)
    backward(mnist)

if __name__=='__main__':
    main()

结果:
深度学习与TensorFlow实战(九)卷积神经网络—Lenet神经网络_第3张图片

第三,测试过程(mnist_lenet5_test.py),对Mnist数据集中的测试数据进行预测,测试模型准确率。具体代码如下所示:

# coding:utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import mnist_lenet5_backward
import numpy as np


# 创建一个默认图,在该图中执行以下操作
def test(mnist):
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32, [
            mnist.test.num_examples,
            mnist_lenet5_forward.IMAGE_SIZE,
            mnist_lenet5_forward.IMAGE_SIZE,
            mnist_lenet5_forward.NUM_CHANNELS])
        y_ = tf.placeholder(tf.float32, [None, mnist_lenet5_forward.OUTPUT_NODE])
        # 训练好的网络,故不使用 dropout
        y = mnist_lenet5_forward.forward(x, False, None)

        ema = tf.train.ExponentialMovingAverage(mnist_lenet5_backward.MOVING_AVERAGE_DECAY)
        ema_restore = ema.variables_to_restore()
        saver = tf.train.Saver(ema_restore)

        # 判断预测值和实际值是否相同
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        # 求平均得到准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        while True:
            with tf.Session() as sess:
                ckpt = tf.train.get_checkpoint_state(mnist_lenet5_backward.MODEL_SAVE_PATH)
                if ckpt and ckpt.model_checkpoint_path:
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    # 根据读入的模型名字切分出该模型是属于迭代了多少次保存的
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    reshaped_x = np.reshape(mnist.test.images, (
                        mnist.test.num_examples,
                        mnist_lenet5_forward.IMAGE_SIZE,
                        mnist_lenet5_forward.IMAGE_SIZE,
                        mnist_lenet5_forward.NUM_CHANNELS))
                    # 利用多线程提高图片和标签的批获取效率
                    #coord = tf.train.Coordinator()  # 3
                    #threads = tf.train.start_queue_runners(sess=sess, coord=coord)  # 4
                    accuracy_score = sess.run(accuracy, feed_dict={x: reshaped_x, y_: mnist.test.labels})
                    print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score))
                    # 关闭线程协调器
                    #coord.request_stop()  # 6
                    #coord.join(threads)  # 7
                else:
                    print('No checkpoint file found')
                    return
            time.sleep(5)


def main():
    mnist = input_data.read_data_sets("./data/", one_hot=True)
    test(mnist)


if __name__ == '__main__':
    main()

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