MNIST数字识别问题

关于··简单的MNIST数据 简单的 show 一下吧

import tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_data#数据mnist = input_data.read_data_sets('MNIST_data',one_hot = True)#打印 training data sizeprint ("training data size:",mnist.train.num_examples)#打印 validating data sizeprint ('validating data size:',mnist.validation.num_examples)#打印 testing data sizeprint('testing data size:',mnist.test.num_examples)# 打印 training dataprint (' example training data \n', mnist.train.images[0])#打印 traing lableprint (' example training data label:',mnist.train.labels[0])

这段代码看得眼花···实在是有点··繁琐····希望以后能把它改得简单一点吧，使用Tensorflow训练神经网络，使用了带指数衰减的学习率设置，使用了正则化来避免过度拟合，以及使用了滑动平均模型来使得最终模型更加健硕

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataINPUT_NODE = 784 # 输入节点数OUTPUT_NODE = 10 # 输出节点数LAYER1_NODE = 500 # 隐含层节点数BATCH_SIZE = 100LEARNING_RETE_BASE = 0.8 # 基学习率LEARNING_RETE_DECAY = 0.99 # 学习率的衰减率REGULARIZATION_RATE = 0.0001 # 正则化项的权重系数TRAINING_STEPS = 10000 # 迭代训练次数MOVING_AVERAGE_DECAY = 0.99 # 滑动平均的衰减系数# 传入神经网络的权重和偏置，计算神经网络前向传播的结果def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):    # 判断是否传入ExponentialMovingAverage类对象    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)# 神经网络模型的训练过程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')    # 定义神经网络结构的参数    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]))    # 计算非滑动平均模型下的参数的前向传播的结果    y = inference(x, None, weights1, biases1, weights2, biases2)    global_step = tf.Variable(0, trainable=False) # 定义存储当前迭代训练轮数的变量    # 定义ExponentialMovingAverage类对象    variable_averages = tf.train.ExponentialMovingAverage(                        MOVING_AVERAGE_DECAY, global_step) # 传入当前迭代轮数参数    # 定义对所有可训练变量trainable_variables进行更新滑动平均值的操作op    variables_averages_op = variable_averages.apply(tf.trainable_variables())    # 计算滑动模型下的参数的前向传播的结果    average_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)    # 定义交叉熵损失值    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)    # 定义L2正则化器并对weights1和weights2正则化    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)    regularization = regularizer(weights1) + regularizer(weights2)    loss = cross_entropy_mean + regularization # 总损失值    # 定义指数衰减学习率    learning_rate = tf.train.exponential_decay(LEARNING_RETE_BASE, global_step,                    mnist.train.num_examples / BATCH_SIZE, LEARNING_RETE_DECAY)    # 定义梯度下降操作op，global_step参数可实现自加1运算    train_step = tf.train.GradientDescentOptimizer(learning_rate)\                         .minimize(loss, global_step=global_step)    # 组合两个操作op    train_op = tf.group(train_step, variables_averages_op)    '''    # 与tf.group()等价的语句    with tf.control_dependencies([train_step, variables_averages_op]):        train_op = tf.no_op(name='train')    '''    # 定义准确率    # 在最终预测的时候，神经网络的输出采用的是经过滑动平均的前向传播计算结果    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    # 初始化回话sess并开始迭代训练    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        # 验证集待喂入数据        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}        # 测试集待喂入数据        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 steps, validation accuracy'                      ' using average model is %f' % (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 steps, test accuracy'              ' using average model is %f' % (TRAINING_STEPS, test_acc))# 主函数def main(argv=None):    mnist = input_data.read_data_sets("MNIST_data", one_hot=True)    train(mnist)# 当前的python文件是shell文件执行的入口文件，而非当做import的python module。if __name__ == '__main__': # 在模块内部执行    tf.app.run() # 调用main函数并传入所需的参数list