Tensorflow：实战Google深度学习框架_第5章，已经过修改改进部分

#第一部分，前向传播
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。在训练神经网络时会创建这些变量；在测试时会通过
#保存的模型加载这些变量的取值。而且更加方便的是，因为可以在变量加载时将滑动平均变量
#重命名，所以可以直接通过同样的名字在训练时使用变量自身，而在测试时使用变量的滑动平均
#值。在这个函数中也会将变量的正则化损失加入损失集合。
def get_weight_variable(shape, regularizer):
    weights = tf.get_variable(
            "weights", shape, 
            initializer=tf.truncated_normal_initializer(stddev=0.1))
    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):
    #第一层
    with tf.variable_scope(\'layer1\'):
        #tf.get_variable/tf.Variable都可以，但是假如多次调用要留意reuse = 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)
    
    #第二层
    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
   
    return layer2

#训练部分
import os
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference

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 = "saves_model_path"
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)
    
    #inference函数
    y = mnist_inference.inference(x, regularizer)
    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())
    
    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, variables_averages_op]):
        train_op = tf.no_op(name = \'train\')
    
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        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})
                                          
            if i % 1000 == 0:
                print("After %d training step(s), loss on training "
                        "batch is %g." % (step, loss_value))
                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()

#评价部分，保存模型
import time 
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference
import mnist_train

# 每10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}
        # 用封装好的函数来计算结果，不关注正则化损失的值
        y = mnist_inference.inference(x, None)
        
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        
        variable_averages = tf.train.ExponentialMovingAverage(
            mnist_train.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)
        
        while True:
            with tf.Session() as sess:
                ckpt = tf.train.get_checkpoint_state(
                    mnist_train.MODEL_SAVE_PATH)
                if ckpt and ckpt.model_checkpoint_path:
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    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()