MNIST_whole_net_XXX

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MNIST训练TensorFlow三层网络

首先是源代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data# about MNIST dataset
INPUT_NODE = 784
OUTPUT_NODE = 10# about the Network
LAYER1_NODE = 500
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99# an assistant function
# Given input and parameters, generate forward propogation.
def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):# if not using ExponentialMovingAverage class, directly use current as parm.if avg_class == None:layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)return tf.matmul(layer1, weights2) + biases2# else use avg_class.average compute EMA, then use for forward propogation.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)# training processes
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')y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='add_1')# hidden layers parametersweights1 = tf.Variable(tf.truncated_normal([INPUT_NODE,LAYER1_NODE], stddev = 0.1))biases1 = tf.Variable(tf.constant(0.1,shape=[LAYER1_NODE]))# output layers parametersweights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE,OUTPUT_NODE], stddev = 0.1))biases2 = tf.Variable(tf.constant(0.1,shape=[OUTPUT_NODE]))# forward propogation without using EMAy = inference(x, None, weights1, biases1, weights2, biases2)# traning timesglobal_step = tf.Variable(0, trainable=False)# given global_step, which will accelerate the process of early trainingvariable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)# tf.trainable_variables() gives collection of GraphKeys.TRAINABLE_VARIABLESvariables_averages_op = variable_averages.apply(tf.trainable_variables())# forward propogation with using EMAaverage_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)# cross entropy loss functioncross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(y, tf.argmax(y_,1))cross_entropy_mean = tf.reduce_mean(cross_entropy)# l2 regularize lossregularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)regularization = regularizer(weights1) + regularizer(weights2)loss = cross_entropy_mean + regularization#exponential decay rate settings# global_step, current iterate step# mnist.train.num_examples / BATCH_SIZE, steps needed for exhausting all datalearning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY)# optimize loss functiontrain_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)# at one time refresh Weights and Exponential_Moving_Average# same as follow:# train_op = tf.group(train_step, variables_averages_op)with tf.control_dependencies([train_step, variables_averages_op]):train_op = tf.no_op(name='train')# examine results for true or negative.correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))# correct precision in this batchaccuracy = tf.reduce_mean( tf.cast(correct_prediction, tf.float32) )with tf.Session() as sess:tf.initialize_all_variables().run()train_feed = {x: mnist.train.images, y:mnist.train.labels}validate_feed = {x: mnist.validation.images, y:mnist.validation.labels}test_feed = {x: mnist.test.images, y_: mnist.test.labels }print test_feed,"\n\n\n"print validate_feed,"\n\n\n"print train_feed,"\n\n\n"# iteratively train NNfor i in range(TRAINING_STEPS):if i % 100 == 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})# after training, validate final precision in testing datasettest_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("/home/user9/DATA/MNIST_manual", one_hot = True)train(mnist)# TensorFlow main app interaction, tf.app.run will call the main() function
if __name__ == '__main__':tf.app.run()

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代码理解

• 首先是整体概览

从这里可以看出网络只有三层：输入层、隐层、输出层：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data# Parameters
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

把数据集按照每100个分成一组：

BATCH_SIZE = 100 

这里制定了指数下降学习率的参数（基础学习率，学习率的衰减率）：

LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99

损失函数中权重正则项所占的lambda，这个值一般通过cross_validation来选取，使得正确率达到最优：

REGULARIZATION_RATE = 0.0001

设定总训练次数：

TRAINING_STEPS = 30000

滑动平均——可以看作训练过程中，对于权重的平均值池化操作。
使用滑动平均模型的训练过程得到的最终模型，在测试数据上具有更好的健壮性——即可以在wild数据上也可以取得好的效果。
滑动平均模型的衰减率：

MOVING_AVERAGE_DECAY = 0.99

构建前向网络的辅助函数（参数：输入，滑动平均类——可选，w1,b1,w2,b2. 可以支持选择是否使用滑动平均模型）：

def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2)

然后是训练函数（需要输入数据库）：

def train(mnist):

主函数，用于作为单文件运行的时候：

def main(argv=None):mnist = input_data.read_data_sets( "/home/user9/DATA/MNIST_manual", one_hot = True)train(mnist)

如果是单文件运行时，执行main()；否则不执行main()，作为函数文件被调用。

if __name__ == '__main__':tf.app.run()

• 然后看train(mnist)的内部。

接下来是我自己总结的简化版本（相当于无语法规范的伪代码，只是为了方便理解。）：

train():x = placeholder()y_ = placeholder()[W1, B1, W2, B2] = random()# 两种前向传播方式# y ——不采用滑动平均y = inference(avr_cls = None)# average_y ——采用滑动平均#为方便理解，这里把global_step写成cur_step，其实就是当前迭代的步数：一步输入一个batchcur_step = 0 # 建立 滑动平均函数（类）var_aver = new_aver_class(MA_decay, cur_step) # 实现 滑动平均 的操作var_aver_op = var_aver -> apply(  {W1, B1, W2, B2} )# 输入了 滑动平均类，调用 "var_aver -> average()"，从而采用 EMA 版本的前向传播average_y = inference(avr_cls = var_aver)# 后向传播  # 平均交叉熵 loss项cross_entropy = softmax_cross_entropy(y, y_,1)cross_entropy_mean = mean( cross_entropy )# 权重 loss项regularizer = l2_regularizer( RGL_RATE )# 建立 正则函数regularization = regularizer( W1, W2 )# 总的 loss functionloss = cross_entropy_mean + regularization# 采用上述 指数下降的学习率 优化# 技巧：指数下降的学习率learning_rate = exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY)# 训练 操作train_step = Optimizer( learning_rate ).minimize( loss, global_step )# 训练 和 滑动平均 操作 放在一块，定义成新的操作：train_optrain_op <--[ train_step, var_aver_op]# 定义 accuracy 运算，得到的是一个 batch上 的正确率accuracy = mean( corrct_prediction )# 初始化 Session，开始训练with tf.Session() as sess:initialize all variables: W1,W2,B1,B2validate_feed = ...test_feed = ...# 迭代训练阶段for i in range(TR_STEPS):# 每1000次迭代 计算一次 accuracyvalidate_acc = sess.run( accuracy ，feed_dict = validate_feed )print validate_acc# 从 mnist 的 train 数据集上 选取 batch_size 个训练数据xs, ys = mnist.train.next_batch( BATCH_SIZE )# 开始运行 train_opsess.run( train_op , feed_dict = {x: xs; y_: ys})# 训练完成后，在测试集上计算 正确率test_acc = sess.run( accuracy , feed_dict = test_feed )print test_acc

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