1、项目介绍：

搭建浅层神经网络完成MNIST数字图像的识别。

2、详细步骤：

（1）将二维图像转成一维，MNIST图像大小为28*28，转成一维就是784。

（2）定义好神经网络的相关参数：

# MNIST数据集相关的常数INPUT_NODE = 784;OUTPUT_NODE = 10;LAYER1_NODE = 500;BATCH_SIZE = 100;LEARNING_RATE_BASE = 0.8;LEARNING_RATE_DECAY = 0.99;REGULARIZATION_RATE = 0.0001;TRAINING_STEPS = 5000;MOVING_ACERTAGE_DECAY = 0.99;

（3）定义一个接口来算神网输出结果，之所以设置这个接口是因为为了适应滑动平均的方法：

def interface(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);

（4）定义训练主函数：

训练主函数按照：输入输出placeholder，各层网络节点权值与偏移量定义，设置滑动平滑，输出两种结果y和acroos_y，定义y的交叉熵和正则化，定义指数衰减学习，训练。

def train(mnist):    x = tf.placeholder(dtype=tf.float32,shape=[None,INPUT_NODE],name="x_input");    y_ = tf.placeholder(dtype=tf.float32,shape=[None,OUTPUT_NODE],name="y_output");        weights1 = tf.Variable(tf.truncated_normal(shape=[INPUT_NODE,LAYER1_NODE],stddev=0.1));    biases1 = tf.Variable(tf.constant(0.1,dtype=tf.float32,shape=[LAYER1_NODE]));        weights2 = tf.Variable(tf.truncated_normal(shape=[LAYER1_NODE,OUTPUT_NODE],stddev=0.1));    biases2 = tf.Variable(tf.constant(0.1,dtype=tf.float32,shape=[OUTPUT_NODE]));        y = interface(x,None,weights1,biases1,weights2,biases2);        global_step = tf.Variable(0,trainable=False);    variable_averages = tf.train.ExponentialMovingAverage(MOVING_ACERTAGE_DECAY,global_step);    variable_averages_op = variable_averages.apply(tf.trainable_variables());    average_y = interface(x,variable_averages,weights1,biases1,weights2,biases2);        # why????????????????????    # 这里的交叉熵是以 y 为标准的    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(REGULARIZATION_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 / BATCH_SIZE,                                              LEARNING_RATE_DECAY);        train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step = global_step);            with tf.control_dependencies([train_step,variable_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 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)));

（5）主函数代码：

def main(argv = None):    mnist = input_data.read_data_sets("C://Users/hasee/TensorFlow/实战TensorFlow代码/datasets/MNIST_data/",                                  one_hot=True);    train(mnist);

（6）运行程序：

if __name__ == "__main__":    main();