mnist数字识别问题

mnist_inference.py

#coding:utf-8
import tensorflow as tf

# 定义神经网络结构相关的参数
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500

# 通过tf.get_variable函数来获取变量
def get_weight_variable(shape, regularizer):
    weights = tf.get_variable(
        "weights", shape,
        initializer=tf.truncated_normal_initializer(stddev = 0.1))

    # 当给出了正则化生成函数时，将当前变量的正则化损失加入名字为losses的集合。
    if regularizer != None:
        tf.add_to_collection('losses', regularizer(weights))
    return weights

# 定义神经网络的前向传播过程
def inference(input_tensor, regularizer):
    # 声明第一层神经网络的变量并完成前向传播过程
    with tf.variable_scope('layer1'):
        # 这里通过tf.get_variable或tf.Variable没有本质区别，因为在训练或者是测试过程中没有在同一个程序中多次调用这个函数
        # 如果在同一个程序中多次调用，在第一次调用之后需要将reuse设置为True
        weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)
        biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)

    #类似的声明第二层神经网络的变量并完成前向传播过程
    with tf.variable_scope('layer2'):
        weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)
        biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))
        layer2 = tf.matmul(layer1, weights) + biases

    #返回最后前向传播的结果
    return layer2

mnist_train.py

#coding:utf-8
import os

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 加载mnist_inference.py中定义的常量和前向传播的函数
import mnist_inference

# 配置神经网络的参数
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARAZTION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99
#模型保存的路径和文件名
MODEL_SAVE_PATH = "model/"
MODEL_NAME = "model.ckpt"

def train(mnist):
    # 定义输入输出placeholder
    x = tf.placeholder(
        tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
    y_ = tf.placeholder(
        tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')

    regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
    #直接使用mnist_inference.py中定义的前向传播过程
    y = mnist_inference.inference(x , regularizer)
    global_step = tf.Variable(0, trainable=False)

    #定义损失函数、学习率、滑动平均操作以及训练过程
    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    variable_averages_op = variable_averages.apply(tf.trainable_variables())
    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)
    loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
    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')

    #初始化Tensorflow持久化类
    saver = tf.train.Saver()
    with tf.Session() as sess:
        tf.initialize_all_variables().run()

        # 在训练过程中不再测试模型在验证数据集上的表现，验证和测试的过程将会有一个独立的程序来完成
        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x:xs, y_:ys})

            # 每1000轮保存一次模型
            if i % 1000 == 0:
                # 输出当前的训练情况。这里只输出了模型在当前训练batch上的损失函数大小。
                # 通过损失函数的大小可以大概了解训练的情况，在验证数据集上的正确率信息可以大概了解训练的情况
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
                #保存当前的模型。这里给出了global_step参数，这样可以让每个被保存模型的文件名末尾加上训练的轮数
                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)

def main(argv=None):
    mnist = input_data.read_data_sets("/tmp/data",one_hot=True)
    train(mnist)

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

mnist_eval.py

#coding:utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 加载mnist_inference.py和mnist_train.py中定义的常量和函数
import mnist_inference
import mnist_train

# 每十秒加载一次最新的模型，并在测试数据上测试最新模型的正确率
EVAL_INTERVAL_SECS = 10

def evaluate(mnist):
    with tf.Graph().as_default() as g:
        # 定义输入输出的格式
        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')
        validate_feed = {x:mnist.validation.images,
                         y_:mnist.validation.labels}

        # 直接通过调用封装好的函数来计算前向传播的结果，因为测试时不关注正则化损失的值，所以这里用于计算正则化损失的函数被设置为None
        y = mnist_inference.inference(x, None)

        # 使用前向传播的结果计算正确率，如果需要对未知的样例进行分类，那么使用tf.argmax(y, 1)就可以得到输入样例的预测类别了
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) #将布尔型转为float

        # 通过变量重命名的方式来加载模型，这样在前向传播的过程中就不需要调用求滑动平均的函数来获取平均值了
        variable_average = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DECAY)
        variable_to_restore = variable_average.variables_to_restore()
        saver = tf.train.Saver(variable_to_restore)

        # 每隔EVAL_INTERVAL_SECS秒调用一次计算正确率的过程以检测训练过程中正确率的变化
        while True:
            with tf.Session() as sess:
                # tf.train.get_checkpoint_state函数会通过checkpoint文件自动找到目录中最新模型的文件名
                ckpt = tf.train.get_checkpoint_state(mnist_train.MODEL_SAVE_PATH)
                if ckpt and ckpt.model_checkpoint_path:
                    # 加载模型
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    # 通过文件名得到模型保存时迭代的轮数
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    accuracy_score = sess.run(accuracy, feed_dict=validate_feed)
                    print("After %s training step(s), validation accuracy = %g"%(global_step, accuracy_score))
                else:
                    print("No checkpoint file found")
                    return

                time.sleep(EVAL_INTERVAL_SECS)

def main(argv=None):
    mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
    evaluate(mnist)

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

代码出自“TensorFlow实战Google深度学习框架”