MNIST手写数据集识别TensorFlow代码实现。

以下是MNIST手写数据集识别TensorFlow实现代码，其中加入正则化过程和指数衰减的学习率设置。参考书籍：Tensorflow 实战Google深度学习框架（第2版）

[code]from tensorflow.examples.tutorials.mnist import input_data

#MINIST相关常数
INPUT_NODE=784
OUTPUT_NODE=10

#配置神经网络的参数
LAYER1_NODE=500
BATCH_SIZE=100
LEARNING_RATE_BASE=0.8
LEARNING_RATE_DECAY=0.99

REGULARIZATON_RATE=0.0001
TRAINING_STEPS=30000
MOVING_AVERAGE_DECAY=0.99

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):
    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)
    
    variables_averages_op=variable_averages.apply(tf.trainable_variables())
    
    
    average_y=inference(x,variable_averages,weights1,biases1,weights2,biases2)
    
    cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))
    cross_entropy_mean=tf.reduce_mean(cross_entropy)
    
    
    regularizer=tf.contrib.layers.l2_regularizer(REGULARIZATON_RATE)
    regularization=regularizer(weights1)+regularizer(weights2)
    loss= cross_entropy_mean+regularization
    
    learning_rate=tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples,LEARNING_RATE_DECAY)
       
    train_step=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)

    with tf.control_dependencies([train_step,variables_averages_op]):
         train_op=tf.no_op(name='train')
    correct_prediction=tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1))
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

    with tf.Session() as sess:
        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 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 accuarcy is %g"%(i,test_acc))
    
def main(argv=None):
    mnist=input_data.read_data_sets("/tmp/data",one_hot=True)
    train(mnist)
    
if __name__=='__main__':
    tf.app.run()