第一张图包括8层LeNet5卷积神经网络的结构图，以及其中最复杂的一层S2到C3的结构处理示意图。

第二张图及第三张图是用tensorflow重写LeNet5网络及其注释。

这是原始的LeNet5网络：

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport time# 声明输入图片数据，类别x = tf.placeholder('float', [None, 784])y_ = tf.placeholder('float', [None, 10])# 输入图片数据转化x_image = tf.reshape(x, [-1, 28, 28, 1])#第一层卷积层，初始化卷积核参数、偏置值，该卷积层5*5大小，一个通道，共有6个不同卷积核filter1 = tf.Variable(tf.truncated_normal([5, 5, 1, 6]))bias1 = tf.Variable(tf.truncated_normal([6]))conv1 = tf.nn.conv2d(x_image, filter1, strides=[1, 1, 1, 1], padding='SAME')h_conv1 = tf.nn.sigmoid(conv1 + bias1)maxPool2 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')filter2 = tf.Variable(tf.truncated_normal([5, 5, 6, 16]))bias2 = tf.Variable(tf.truncated_normal([16]))conv2 = tf.nn.conv2d(maxPool2, filter2, strides=[1, 1, 1, 1], padding='SAME')h_conv2 = tf.nn.sigmoid(conv2 + bias2)maxPool3 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')filter3 = tf.Variable(tf.truncated_normal([5, 5, 16, 120]))bias3 = tf.Variable(tf.truncated_normal([120]))conv3 = tf.nn.conv2d(maxPool3, filter3, strides=[1, 1, 1, 1], padding='SAME')h_conv3 = tf.nn.sigmoid(conv3 + bias3)# 全连接层# 权值参数W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 120, 80]))# 偏置值b_fc1 = tf.Variable(tf.truncated_normal([80]))# 将卷积的产出展开h_pool2_flat = tf.reshape(h_conv3, [-1, 7 * 7 * 120])# 神经网络计算，并添加sigmoid激活函数h_fc1 = tf.nn.sigmoid(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)# 输出层，使用softmax进行多分类W_fc2 = tf.Variable(tf.truncated_normal([80, 10]))b_fc2 = tf.Variable(tf.truncated_normal([10]))y_conv = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)# 损失函数cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))# 使用GDO优化算法来调整参数train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)sess = tf.InteractiveSession()# 测试正确率correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))# 所有变量进行初始化sess.run(tf.initialize_all_variables())# 获取mnist数据mnist_data_set = input_data.read_data_sets('MNIST_data', one_hot=True)# 进行训练start_time = time.time()for i in range(20000):    # 获取训练数据    batch_xs, batch_ys = mnist_data_set.train.next_batch(200)    # 每迭代100个 batch，对当前训练数据进行测试，输出当前预测准确率    if i % 2 == 0:        train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})        print("step %d, training accuracy %g" % (i, train_accuracy))        # 计算间隔时间        end_time = time.time()        print('time: ', (end_time - start_time))        start_time = end_time    # 训练数据    train_step.run(feed_dict={x: batch_xs, y_: batch_ys})# 关闭会话sess.close()

下面是改进后的LeNet5网络：

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport timeimport matplotlib.pyplot as plt# 初始化单个卷积核上的权重def weight_variable(shape):    initial = tf.truncated_normal(shape, stddev=0.1)    return tf.Variable(initial)# 初始化单个卷积核上的偏置值def bias_variable(shape):    initial = tf.constant(0.1, shape=shape)    return tf.Variable(initial)# 输入特征x，用卷积核W进行卷积运算，strides为卷积核移动步长，# padding表示是否需要补齐边缘像素使输出图像大小不变def conv2d(x, W):    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')# 对x进行最大池化操作，ksize进行池化的范围，def max_pool_2x2(x):    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')sess = tf.InteractiveSession()# 声明输入图片数据，类别x = tf.placeholder('float32', [None, 784])y_ = tf.placeholder('float32', [None, 10])# 输入图片数据转化x_image = tf.reshape(x, [-1, 28, 28, 1])W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)W_conv2 = weight_variable([5, 5, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)W_fc1 = weight_variable([7 * 7 * 64, 1024])# 偏置值b_fc1 = bias_variable([1024])# 将卷积的产出展开h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])# 神经网络计算，并添加relu激活函数h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)W_fc2 = weight_variable([1024, 128])b_fc2 = bias_variable([128])h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)W_fc3 = weight_variable([128, 10])b_fc3 = bias_variable([10])y_conv = tf.nn.softmax(tf.matmul(h_fc2, W_fc3) + b_fc3)# 代价函数cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))# 使用Adam优化算法来调整参数train_step = tf.train.GradientDescentOptimizer(1e-5).minimize(cross_entropy)# 测试正确率correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float32"))# 所有变量进行初始化sess.run(tf.initialize_all_variables())# 获取mnist数据mnist_data_set = input_data.read_data_sets('MNIST_data', one_hot=True)c = []# 进行训练start_time = time.time()for i in range(1000):    # 获取训练数据    batch_xs, batch_ys = mnist_data_set.train.next_batch(200)    # 每迭代10个 batch，对当前训练数据进行测试，输出当前预测准确率    if i % 2 == 0:        train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})        c.append(train_accuracy)        print("step %d, training accuracy %g" % (i, train_accuracy))        # 计算间隔时间        end_time = time.time()        print('time: ', (end_time - start_time))        start_time = end_time    # 训练数据    train_step.run(feed_dict={x: batch_xs, y_: batch_ys})sess.close()plt.plot(c)plt.tight_layout()