最近课程要求实现手写识别任务，于是就学习了网络上和已有的相关程序，编写了一份Tensorfliow的手写识别程序，准确率超过98%。之前也写过一份Pytorch版本的代码，有需要的同学也可以自取~。希望可以和大家多多交流~

print_activations（用于展示每一特征图尺寸）

inference（搭建网络结构）

do_train（完成网络训练）

（1）INPUT（输入层）：输入图片：28∗28

（2） C1(卷积层)：卷积核：5*5*32、输出特征图：24*24*32

（3）S2（池化层）：输出：12*12*32

（4）C3（卷积层）：卷积核：5*5*64、输出特征图：8*8*64

（5）S4（池化层）：输出：4*4*64

（6）C5（卷积层）：卷积核 4*4*256、输出特征图 1*1*256(256维的向量)

（7） F6（全连接层）：256 —> 128

（8）Output（输出层）：128 —> 10

# from sklearn.datasets import fetch_mldataimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data import matplotlib.pyplot as pltimport numpy as npfrom math import floor, ceil# 超参数 这组参数也超过98%MNIST_data_path = &＃39;MNIST/&＃39; # 数据路劲REGULARIZATION_RATE = 0.000001 # 正则化系数MOVING_AVERAGE_DECAY = 0.999 # 滑动平均DROPOUT_RATE= 0.50 # dropout系数 LEARNING_RATE_BASE = 0.0015 # 初始学习率LEARNING_RATE_DECAY = 0.95 # 学习率衰减系数DELAY_steps = 1000BATCH_SIZE = 400 # 批T处理数量TRAINING_STEPS = 2000 # 迭代次数def print_activations(t):##展示每一层卷积层 或池化层输出tensor的尺寸print(t.op.name,&＃39;&＃39;,t.get_shape().as_list())##输出名字和尺寸return# 构建模型，train控制是否dropoutdef inference(x, dropout_rate, regularizer):print_activations(x)# 第一层卷积：输入1通道，输出6通道，卷积核5*5with tf.variable_scope(&＃39;layer1-conv1&＃39;, reuse = tf.AUTO_REUSE):conv1_weights = tf.get_variable("weight", [5, 5, 1, 32],initializer=tf.truncated_normal_initializer(stddev=0.1)) # 生成随机值，服从标准偏差为0.1 initializer=tf.truncated_normal_initializer(stddev=0.1)conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0)) # 偏差，初始化为常数0conv1_wx = tf.nn.conv2d(x, conv1_weights, strides=[1, 1, 1, 1], padding=&＃39;VALID&＃39;) # 进行卷积操作，strides 卷积时在图像每一维的步长，不考虑边界conv1 = tf.nn.relu(tf.nn.bias_add(conv1_wx, conv1_biases)) # 卷积的结果加上偏差（矩阵+向量的操作），再进行ReLuprint_activations(conv1)conv1=tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding=&＃39;VALID&＃39;, name=&＃39;max_pooling&＃39;) # 进行最大池化操作print_activations(conv1)# 第二层卷积：输入6通道，输出16通道，卷积核5*5with tf.variable_scope("layer2-conv2", reuse = tf.AUTO_REUSE):conv2_weights = tf.get_variable("weight", [5, 5, 32, 64],initializer=tf.truncated_normal_initializer(stddev=0.1))conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))conv2_wx = tf.nn.conv2d(conv1, conv2_weights, strides=[1, 1, 1, 1], padding=&＃39;VALID&＃39;)conv2 = tf.nn.relu(tf.nn.bias_add(conv2_wx, conv2_biases))print_activations(conv2)conv2=tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding=&＃39;VALID&＃39;,name=&＃39;max_pooling&＃39;)print_activations(conv2)reshaped = tf.reshape(conv2, [-1, 64*4*4]) # 转化成为向量# 全连接层1：16*4*4 ——> 120with tf.variable_scope(&＃39;layer-fc1&＃39;, reuse = tf.AUTO_REUSE):fc1_weights = tf.get_variable("weight", [64*4*4, 256],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection(&＃39;losses&＃39;, regularizer(fc1_weights)) # 正则化fc1_biases = tf.get_variable("bias", [256], initializer=tf.constant_initializer(0.1))fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)fc1 = tf.nn.dropout(fc1, dropout_rate) # dropoutprint_activations(fc1)# 全连接层2：120 ——> 84with tf.variable_scope(&＃39;layer-fc2&＃39;, reuse=tf.AUTO_REUSE):fc2_weights = tf.get_variable("weight", [256, 128],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection(&＃39;losses&＃39;, regularizer(fc2_weights))fc2_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.1))fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)fc2 = tf.nn.dropout(fc2, dropout_rate)print_activations(fc2)# 全连接层3：84 ——> 10with tf.variable_scope(&＃39;layer-fc3&＃39;, reuse=tf.AUTO_REUSE):fc3_weights = tf.get_variable("weight", [128, 10],initializer=tf.truncated_normal_initializer(stddev=0.1))# if regularizer != None: tf.add_to_collection(&＃39;losses&＃39;, regularizer(fc3_weights))fc3_biases = tf.get_variable("bias", [10], initializer=tf.constant_initializer(0.1))# fc3 = tf.nn.relu(tf.matmul(fc2, fc3_weights) + fc3_biases)fc3 = tf.nn.softmax(tf.matmul(fc2, fc3_weights) + fc3_biases)print_activations(fc3)return fc3# 训练网络模型def do_train(train_x, train_y, test_x, test_y):tf.reset_default_graph() # 清除默认图形堆栈并重置全局默认图形test_y_labelNumber = np.argmax(test_y, axis=1) # 获取label的数值NUM_TEST = len(test_y)NUM_TRAIN = len(train_y)with tf.name_scope(&＃39;input&＃39;):x = tf.placeholder(tf.float32, [None, 28, 28, 1], name=&＃39;x-input&＃39;)y_ = tf.placeholder(tf.float32, [None, 10], name=&＃39;y-input&＃39;)# tf.placeholder()函数是在神经网络构建graph的时候在模型中的占位，此时并没有把要输入的数据传入模型，# 它只会分配必要的内存。等建立session后，运行模型时通过feed_dict()函数向占位符喂入数据。# regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)y = inference(x, dropout_rate=DROPOUT_RATE, regularizer = None) # dropout策略及正则化策略防止过拟合, y_是经过网络前向传播后的预测输出值global_step = tf.Variable(0, trainable=False)# 定义滑动平均操作、损失函数、学习率、训练过程。with tf.variable_scope("moving_average", reuse = tf.AUTO_REUSE): # 处理滑动平均的命名空间variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) # 实现滑动平均模型和计算变量的移动平均值。variables_averages_op = variable_averages.apply(tf.trainable_variables())with tf.variable_scope("loss_function", reuse = tf.AUTO_REUSE): # 计算损失函数的命名空间。# loss = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) # 交叉熵loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y,labels=y_)) # 交叉熵# loss = -tf.reduce_sum(y_ * tf.log(y))tf.summary.scalar("loss", loss) # 记录losswith tf.variable_scope("train_step", reuse = tf.AUTO_REUSE):# 定义学习率、优化方法及每一轮执行训练的操作的命名空间。#exponential_decay()学习率衰减函数learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,DELAY_steps,LEARNING_RATE_DECAY,staircase=True) #每1000(DELAY_steps)轮训练后要乘以学习率的衰减值 #train.num_examples / BATCH_SIZE, train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step)with tf.control_dependencies([train_step, variables_averages_op]):train_op = tf.no_op(name=&＃39;train&＃39;) cOnfig= tf.ConfigProto()config.gpu_options.allow_growth = True #允许显存增长config.gpu_options.per_process_gpu_memory_fraction = 0.9 #不完全分配所有的GPU内存,设为90%log_device_placement=True with tf.Session(cOnfig= config) as sess:tf.global_variables_initializer().run() #sess.run(tf.global_variables_initializer()) 初始化变量for i in range(TRAINING_STEPS + 1):# 随机取100训练数据index_batch = np.random.randint(NUM_TRAIN, size=BATCH_SIZE)xs,ys = train_x[index_batch], train_y[index_batch]##训练集re_xs = np.reshape(xs, [-1, 28, 28, 1])re_ys = np.reshape(ys, [-1, 10])# 训练集 喂数据 train_feed={x: re_xs, y_: re_ys} # 执行训练_, loss_value, step = sess.run([train_step, loss, global_step], feed_dict=train_feed)if i % (100) == 0:# 测试集测试# index_batch = np.random.randint(NUM_TEST, size=BATCH_SIZE)# xs_, ys_ = test_x[index_batch], test_y[index_batch]xs_, ys_ = test_x, test_yre_xs_ = np.reshape(xs_, [-1, 28, 28, 1])re_ys_ = np.reshape(ys_, [-1, 10])# 测试集 喂数据test_feed = {x: re_xs_, y_: re_ys_} loss_value_, y_pre = sess.run([loss, y], feed_dict=test_feed)y_pre_labelNumber = np.argmax(y_pre, axis=1)correct = 0for j in range(0, NUM_TEST):if y_pre_labelNumber[j] == test_y_labelNumber[j]:correct += 1print("训练过程中, 当Train_Step = %d 时, trainloss = %g, testloss = %g, 测试准确率为 = %g%%" % (step, loss_value, loss_value_, float(correct)*100/NUM_TEST))print("-" * 50)returnif __name__ == &＃39;__main__&＃39;: mnist = input_data.read_data_sets(MNIST_data_path,one_hot=True) # 导入数据集 train_x, train_y, test_x, test_y = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels # 读取数据do_train(train_x, train_y, test_x, test_y)