[ MOOC课程学习 ] 人工智能实践：Tensorflow笔记_CH5_2/3 实现手写体 mnist 数据集的识别任务

实现手写体 mnist 数据集的识别任务,共分为三个模块文件,分别是:

描述网络结构的前向传播过程文件(mnist_forward.py)
描述网络参数优化方法的反向传播过程文件( mnist_backward.py )
验证模型准确率的测试过程文件(mnist_test.py)。

1. 描述网络结构的前向传播过程文件(mnist_forward.py)

   
   #coding:utf-8
   
   import tensorflow as tf
   
   INPUT_NODE = 784
   OUTPUT_NODE = 10
   LAYER1_NODE = 500
   
   def get_weight(shape, regularizer):
       w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
       if regularizer != None:
           tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
       return w
   
   def get_bias(shape):
       b = tf.Variable(tf.zeros(shape))
       return b
   
   def forward(x, regularizer):
       w1 = get_weight([INPUT_NODE, LAYER1_NODE], regularizer)
       b1 = get_bias([LAYER1_NODE])
       y1 = tf.nn.relu(tf.matmul(x, w1) + b1)
   
       w2 = get_weight([LAYER1_NODE, OUTPUT_NODE], regularizer)
       b2 = get_bias([OUTPUT_NODE])
       y = tf.matmul(y1, w2) + b2
   
       return y
   

2. 描述网络参数优化方法的反向传播过程文件( mnist_backward.py )

   
   #coding:utf-8
   
   import tensorflow as tf
   from tensorflow.examples.tutorials.mnist import input_data
   import os
   import mnist_forward
   
   
   BATCH_SIZE = 200
   REGULARIZER = 0.0001
   LR = 0.1
   LR_DECAY_RATE = 0.99
   EMA_DECAY = 0.99
   STEPS = 50000
   MODEL_SAVE_PATH = './model/'
   MODEL_NAME = 'mnist_model'
   
   def backward(mnist):
   
       x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
       y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
       y = mnist_forward.forward(x, REGULARIZER)
   
       global_step = tf.Variable(0, trainable=False)
   
       lr = tf.train.exponential_decay(
           learning_rate = LR,
           global_step = global_step,
           decay_steps = mnist.train.num_examples / BATCH_SIZE,
           decay_rate = LR_DECAY_RATE,
           staircase = True
       )
   
       ce = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(y_, 1), logits=y)
       cem = tf.reduce_mean(ce)
       loss = cem + tf.add_n(tf.get_collection('losses'))
       train_step = tf.train.GradientDescentOptimizer(lr).minimize(loss, global_step=global_step)
   
       ema = tf.train.ExponentialMovingAverage(
           decay = EMA_DECAY,
           num_updates = global_step
       )
       ema_op = ema.apply(tf.trainable_variables())
   
       with tf.control_dependencies([train_step, ema_op]):
           train_op = tf.no_op('train')
   
       saver = tf.train.Saver()
   
       with tf.Session() as sess:
           sess.run(tf.global_variables_initializer())
           for i in range(STEPS):
               xs, ys = mnist.train.next_batch(BATCH_SIZE)
               _, loss_v, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})
               if i % 1000 == 0:
                   print('After %d training steps, loss on training batch is %g.' % (step, loss_v))
                   saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)
   
   def main():
       mnist = input_data.read_data_sets('./data/', one_hot=True)
       backward(mnist)
   
   if __name__ == '__main__':
       main()
   
   

   model 文件夹：
   

3. 验证模型准确率的测试过程文件(mnist_test.py)。

   
   #coding:utf-8
   
   import time
   import tensorflow as tf
   from tensorflow.examples.tutorials.mnist import input_data
   import mnist_forward
   import mnist_backward
   TEST_INTERVAL_SECS = 5
   
   def test(mnist):
       with tf.Graph().as_default() as g:
           x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
           y_ = tf.placeholder(tf.float32, [None, mnist_forward.OUTPUT_NODE])
           y = mnist_forward.forward(x, None)
   
           # 实例化可还原滑动平均的 saver
           # 这样所有参数在会话中被加载时会被赋值为各自的滑动平均值
           ema = tf.train.ExponentialMovingAverage(mnist_backward.EMA_DECAY)
           ema_restore = ema.variables_to_restore()
           saver = tf.train.Saver(ema_restore)
   
           # 计算正确率
           correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
           accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   
           while True:
               with tf.Session() as sess:
                   # 加载训练好的模型
                   ckpt = tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH)
                   # 如果已有 ckpt 模型则恢复
                   if ckpt and ckpt.model_checkpoint_path:
                       # 恢复会话
                       saver.restore(sess, ckpt.model_checkpoint_path)
                       # 恢复轮数
                       global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                       # 计算正确率
                       accuracy_score = sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
                       print('After %s training steps, test accuracy = %g' % (global_step, accuracy_score))
                   else:
                       print('No checkpoint file found.')
                       return
               time.sleep(TEST_INTERVAL_SECS)
   
   def main():
       mnist = input_data.read_data_sets('./data/', one_hot=True)
       test(mnist)
   
   if __name__ == '__main__':
       main()