MNIST识别数字(TensorFlow框架)

     本篇文章主要实现在TensorFlow平台下识别MNIST数据集上的0-9十个数字，通过随机梯度下降算法优化参数，准确率在30000次迭代后保持在98.4%。

     下面是完整的代码：

     

'''MNIST数字识别问题'''import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataINPUT_NODE = 784   #输入层节点数OUTPUT_NODE = 10   #输出层节点数LAYER1_NODE = 500   #隐藏层节点数 BATCH_SIZE = 100   #一个batch中训练数据的个数 LEARNING_RATE_BASE = 0.8  #初始学习率 LEARNING_RATE_DECAY = 0.99  #学习率的衰减率 REGULARIZATION_RATE = 0.0001  #描述模型复杂度的正则化在损失函数中的系数 TRAINING_STEPS = 30000       #训练轮数 MOVING_AVERAGE_DECAY = 0.99   #滑动平均衰减率 '''计算神经网络的前向传播结果'''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)'''训练模型的过程'''def train(mnist):    x = tf.placeholder(tf.float32,[None,INPUT_NODE],name='x-input')    y_ = tf.placeholder(tf.float32,[None,OUTPUT_NODE],name='y-input')    weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE,LAYER1_NODE],stddev=0.1))    biases1 = tf.Variable(tf.constant(0.1,shape=[LAYER1_NODE]))    weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE,OUTPUT_NODE],stddev=0.1))    biases2 = tf.Variable(tf.constant(0.1,shape=[OUTPUT_NODE]))    y =inference(x,None,weights1,biases1,weights2,biases2)    global_step = tf.Variable(0,trainable=False)    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)     variable_averages_op = variable_averages.apply(tf.trainable_variables())         average_y = inference(x,variable_averages,weights1,biases1,weights2,biases2)        '''计算交叉謪'''    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(y_,1),logits=y)    cross_entropy_mean = tf.reduce_mean(cross_entropy)        '''计算L2正则化损失函数'''    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)    regularization = regularizer(weights1) + regularizer(weights2)    loss = cross_entropy_mean + regularization  #损失函数等于两部分相加    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY)    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)    train_op = tf.group(train_step,variable_averages_op)        correct_prediction = tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))    '''初始化会话并开始训练过程'''    with tf.Session() as sess:        #tf.initialize_all_variables().run()        tf.global_variables_initializer().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):            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})            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()

 

      运行结果：