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人工智能实践：Tensorflow笔记 # 10 卷积神经网络 LeNet-5

news 来源：原创 2024/5/19 7:44:31

LeNet 神经网络是 Yann LeCun 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 $32 * 32 * 1$ 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 6，步长为 1，非全零填充模式；
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 1，全零填充模式；
⑤进行卷积，卷积核大小为 $5 * 5 * 6$ ，个数为 16，步长为 1，非全零填充模式；
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 1，全零填充模式；
⑧全连接层进行 10 分类。

Lenet 神经网络的输入是 32*32*1，经过 5*5*1 的卷积核，卷积核个数为 6 个，采用非全零填充方式，步长为 1，根据非全零填充计算公式：输出尺寸=（输入尺寸-卷积核尺寸+1）/步长=（32-5+1）/1=28.故经过卷积后输出为 28*28*6。经过第一层池化层，池化大小为 2*2，全零填充，步长为 2，由全零填充计算公式：输出尺寸=输入尺寸/步长=28/2=14，池化层不改变深度，深度仍为 6。用同
样计算方法，得到第二层池化后的输出为 5*5*16。将第二池化层后的输出拉直送入全连接层。

根据 Lenet 神经网络的结构可得，Lenet 神经网络具有如下特点：
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 神经网络进行微调，使其适应 Mnist 数据集：
由于 Mnist 数据集中图片大小为 $28 * 28 * 1$ 的灰度图片，而 Lenet 神经网络的输入为 $32 * 32 * 1$ ,故需要对 Lenet 神经网络进行微调。
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 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）

mnist_lenet5_forward.py

#coding:utf-8
import tensorflow as tf
IMAGE_SIZE = 28
NUM_CHANNELS = 1
CONV1_SIZE = 5
CONV1_KERNEL_NUM = 32
CONV2_SIZE = 5
CONV2_KERNEL_NUM = 64
FC_SIZE = 512
OUTPUT_NODE = 10

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

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

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

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):
    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)
    relu1 = tf.nn.relu(tf.nn.bias_add(conv1,conv1_b))
    pool1 = max_pool_2x2(relu1)

    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)

    pool_shape = pool2.get_shape().as_list()
    nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
    reshaped = tf.reshape(pool2,[pool_shape[0],nodes])

    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)
    if train: fc1 = tf.nn.dropout(fc1,0.5)

    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

mnist_lenet5_backward.py

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

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.005
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
STEPS = 50000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "./model/"
MODEL_NAME = "mnist_model"

def backward(mnist):
    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)
    global_step = tf.Variable(0,trainable=False)

    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))
    cem = tf.reduce_mean(ce)
    loss = cem + tf.add_n(tf.get_collection('losses'))

    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE,
        LEARNING_RATE_DECAY,
        staircase=True
    )

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)

    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
    ema_op = ema.apply(tf.trainable_variables())
    with tf.control_dependencies([train_step,ema_op]):
        train_op = tf.no_op(name='train')

    saver = tf.train.Saver()

    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()
        sess.run(init_op)

        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(STEPS):
            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_op,loss,global_step],feed_dict={x:reshaped_xs,y_:ys})
            if i % 100 == 0:
                print("After %d training step(s),loss on training batch 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()

mnist_lenet5_test.py

#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

TEST_INTERVAL_SECS = 5

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])
        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)

        correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
        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)

                    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
                        )
                    )
                    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))
                else:
                    print("No checkpoint file found")
            time.sleep(TEST_INTERVAL_SECS)

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

if __name__ == "__main__":
    main()