Tensorflow-MNIST数字识别练习代码

Tensorflow-MNIST数字识别练习代码

方案一 训练代码 + 验证代码

# -- coding: utf-8 --import tensorflow as tf  from tensorflow.examples.tutorials.mnist import input_data  #层节点INPUT_NODE = 784  LAYER1_NODE = 500OUTPUT_NODE = 10  #数据batch大小BATCH_SIZE = 100    #训练参数LEARNING_RATE_BASE = 0.8   LEARNING_RATE_DECAY = 0.99   REGULARIZATION_RATE= 0.0001   TRAINING_STEPS = 30000     MOVING_AVERAGE_DECAY = 0.99   #前向传播函数  def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):      if avg_class == None:          layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)          return tf.matmul(layer1, weights2) + biases2      else:          layer1 = tf.nn.relu(tf.matmul(input_tensor,avg_class.average(weights1))+avg_class.average(biases1))          return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)      def train(mnist): #输入层和数据label     x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')      y_ = tf.placeholder(tf.float32, [None,OUTPUT_NODE], name='y-input')#隐藏层参数初始化        weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE,LAYER1_NODE], stddev=0.1))      biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))  #输出层参数初始化     weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))      biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))  #前向传播结果y      y = inference(x, None, weights1, biases1, weights2, biases2)#use for count the train step , trainable=False    global_step = tf.Variable(0, trainable=False)  #滑动平均模型，及加入滑动平均的前向传播结果average_y    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)      variables_averages_op = variable_averages.apply(tf.trainable_variables())       average_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)  #计算交叉熵，并加入正则-->损失函数loss    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y)    cross_entropy_mean = tf.reduce_mean(cross_entropy)       regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)      regularization = regularizer(weights1) + regularizer(weights2)       loss = cross_entropy_mean + regularization  #学习率    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY)  #train_step 梯度下降(学习率，损失函数，全局步数)      train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)  #运算图控制，用train_op作集合      with tf.control_dependencies([train_step, variables_averages_op]):          train_op = tf.no_op(name='train')  #判断准确率      correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))      accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))#持久化    saver = tf.train.Saver()        with tf.Session() as sess:          tf.initialize_all_variables().run()          validate_feed = {x:mnist.validation.images,y_:mnist.validation.labels}          test_feed = {x:mnist.test.images,y_:mnist.test.labels}            for i in range(TRAINING_STEPS): #每1000轮测试一次                     if i%1000 == 0:                  validate_acc = sess.run(accuracy, feed_dict=validate_feed)                  print("After %d training step(s), validation accuracy using average model is %g " %(i,validate_acc))                xs,ys = mnist.train.next_batch(BATCH_SIZE)              sess.run(train_op, feed_dict={x:xs, y_:ys})          saver.save(sess,"./model/model.ckpt")        test_acc = sess.run(accuracy, feed_dict=test_feed)          print("After %d training step(s), test accuracy using average model is %g" %(TRAINING_STEPS, test_acc))      def main(argv=None):      mnist = input_data.read_data_sets("mnist_data/", one_hot=True)      train(mnist)    if __name__== '__main__':      tf.app.run()

方案二 训练 + 验证代码

文件 mnis_inference.py

# -- coding: utf-8 --import tensorflow as tf  #层节点INPUT_NODE = 784  LAYER1_NODE = 500OUTPUT_NODE = 10  #获取权值weightsdef get_weight_variable(shape, regularizer):    weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))        if regularizer != None:        tf.add_to_collection('losses', regularizer(weights))    print("test_test")            return weights#前向传播函数  def inference(input_tensor, regularizer):#layer1    with tf.variable_scope('layer1'):        weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)        biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))           layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases) #layer2      with tf.variable_scope('layer2'):        weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)        biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))           layer2 = tf.matmul(layer1, weights) + biases    return layer2

文件 mnist_train.py

# -- coding: utf-8 --import osimport tensorflow as tf  from tensorflow.examples.tutorials.mnist import input_data  import mnist_inference  #数据batch大小BATCH_SIZE = 100    #训练参数LEARNING_RATE_BASE = 0.8   LEARNING_RATE_DECAY = 0.99   REGULARIZATION_RATE= 0.0001   TRAINING_STEPS = 30000     MOVING_AVERAGE_DECAY = 0.99   #模型保存路径及文件名MODEL_SAVE_PATH = "/model2/"MODEL_NAME = "model.ckpt"  def train(mnist): #输入层和数据label     x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')      y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')#前向传播结果y    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)      y = mnist_inference.inference(x, regularizer)    global_step = tf.Variable(0, trainable=False)  #滑动平均模型    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)      variables_averages_op = variable_averages.apply(tf.trainable_variables())   #计算交叉熵，并加入正则-->损失函数loss    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y)    cross_entropy_mean = tf.reduce_mean(cross_entropy)       loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))  #学习率    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY)  #train_step 梯度下降(学习率，损失函数，全局步数)      train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)  #运算图控制，用train_op作集合      with tf.control_dependencies([train_step, variables_averages_op]):          train_op = tf.no_op(name='train')  #持久化    saver = tf.train.Saver()        with tf.Session() as sess:          tf.initialize_all_variables().run()           for i in range(TRAINING_STEPS):             xs, ys = mnist.train.next_batch(BATCH_SIZE)            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x:xs, y_:ys})#每1000轮保存一次                     if i%1000 == 0:                  print("After %d training step(s), loss on training batch is %g " %(step, loss_value))                  saver.save(sess, "./model2/model.ckpt")    def main(argv=None):      mnist = input_data.read_data_sets("mnist_data/", one_hot=True)      train(mnist)    if __name__== '__main__':      tf.app.run()

文件 mnist_eval.py

# -- coding: utf-8 --import osimport timeimport tensorflow as tf  from tensorflow.examples.tutorials.mnist import input_data  import mnist_inference  import mnist_trainEVAL_INTERVAL_SECS = 10 #模型保存路径及文件名MODEL_SAVE_PATH = "/model2/"MODEL_NAME = "model.ckpt"def evaluate(mnist):    with tf.Graph().as_default() as g:        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')        validate_feed = {x: mnist.validation.images, y_ :mnist.validation.labels}             y = mnist_inference.inference(x, None)             correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))             variable_averages = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DECAY)        variables_to_restore = variable_averages.variables_to_restore()        saver = tf.train.Saver(variables_to_restore)        with tf.Session() as sess:            saver.restore(sess, "./model2/model.ckpt")                    accuracy_score = sess.run(accuracy, feed_dict=validate_feed)            print("**********accuracy = %g", accuracy_score)                                   def main(argv=None):    mnist = input_data.read_data_sets("mnist_data/", one_hot=True)    evaluate(mnist)    if __name__== '__main__':      tf.app.run()