tensorflow学习（2）TensorFlow Mechanics 101

女爷i 2022-06-08 04:50 213阅读 0赞

# tensorflow(2) TensorFlow Mechanics 101 #

标签（空格分隔）： tensorflow

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参考 [TF英文社区][TF][TF中文社区][TF 1]

fully\_connected\_feed.py 是总体的运行过程  
mnist.py中定义了四个函数，inference，training，loss，evaluation

## mnist.py ##

### 一、inference ###

就是网络结构函数，mnist.py中的inference定义的网络有一对全连接层，和一个有10个线性节点的线性层

*  input:inference输入placeholder和第一层，第二层网络hidden units的个数
 *  每一层都有唯一的name\_scope，所有的item都创建在这个namescope下，相当于给这一层的所有item加了一个前缀

with tf.name_scope('hidden1'):

*  在每一个scope中，weight和biase由tf.Variable生成，大小根据（输入输出）的维度设置  
    weight=\[connect from,connect to\]  
    biase=\[connect to\]
 *  每个变量在创建时，都会被给予一个初始化操作

weights = tf.Variable(
        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')

比如weights会用tf.truncated\_normal初始化器，根据给定的均值和标准差生成一个随机分布  
biase根据tf.zeros保证它们的初始值都是0。

graph中主要有三个operation，两个tf.nn.relu和一个tf.matmul  
最后，程序会返回包含了输出结果的logits Tensor。

### 二、loss ###

loss() 也是graph的一部分，输入两个参数，神经网络的分类结果和labels正确结果。进行比较，计算损失。

def loss(logits, labels):
      labels = tf.to_int64(labels)
      cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
          labels=labels, logits=logits, name='xentropy')
      return tf.reduce_mean(cross_entropy, name='xentropy_mean')

这个函数分为三步

*  1.先将labels转换成所需要的格式  
    `tf.to_int64(labels)`这个操作可以将labels抓换成指定的格式1-hot labels，  
    1-hot labels：例如，如果类标识符为“3”，那么该值就会被转换为：  
    `[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]`  
    将inference的判断结果和labels进行比较
 *  2.利用函数tf.nn.sparse\_softmax\_cross\_entropy\_with\_logits 计算交叉熵
 *  3.计算一个batch的平均loss

loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')

tf.reduce\_mean函数(可跨维度的计算平均值)，计算batch维度（第一维度）下交叉熵（cross entropy）的平均值，将将该值作为总损失

### 三、training ###

*  input: loss tensor ， learning rate  
    主要分为四步
 *  1、 创建一个summarizer， 用来更新损失，summary的值会被写在events file里面  
    `tf.summary.scalar('loss', loss)`
 *  2、创建一个optimizer优化器对象tf.train.GradientDescentOptimizer（设置学习率）
 *  3、创建global\_step变量 ，用于记录全局训练步骤的单值
 *  4、开始优化 optimizer.minimize（输入loss 和 global step）
 *  return train\_op

### 四、evaluation ###

输入网络的分类结果和labels，和loss函数的输入一样

def evaluation(logits, labels):
    
      """Evaluate the quality of the logits at predicting the label.
    
      Args:
        logits: Logits tensor, float - [batch_size, NUM_CLASSES].
        labels: Labels tensor, int32 - [batch_size], with values in the
          range [0, NUM_CLASSES).
    
      Returns:
        A scalar int32 tensor with the number of examples (out of batch_size)
        that were predicted correctly.
      """
      # For a classifier model, we can use the in_top_k Op.
      # It returns a bool tensor with shape [batch_size] that is true for
      # the examples where the label is in the top k (here k=1)
      # of all logits for that example.
      correct = tf.nn.in_top_k(logits, labels, 1)
      # Return the number of true entries.
      return tf.reduce_sum(tf.cast(correct, tf.int32))

## fully\_connected\_feed.py ##

一旦图建立完之后，就可以在循环训练和评估  
tensorflow/tensorflow/examples/tutorials/mnist/fully\_connected\_feed.py

### 总体步骤 ###

1、设置输入  
定义placeholder，函数`def placeholder_inputs(batch_size)`  
2、开始训练run\_rainning

*  读入数据集
 *  建立图
 *  创建session
 *  初始化
 *  开始循环训练  
    
    
     *  check status
     *  do evaluation

### 1.place holder ###

### 2.the graph ###

建图中所有的操作都是在`with tf.Graph（）.as_default()`下进行的  
tf.graph可能会执行所有的ops，可以包含多个图，创建多个线程  
我们只需要一个single graph

### 3.session ###

在定义完图后，需要创建一个会话session来开启这个图  
\- 创建session `sess=tf.session()`  
\- 创建initializer, `initializer=tf.global_variables_initializer`  
\- sess.run(initializer) 会自动初始化所有的变量

### 4.training loop ###

在变量初始化完成之后，就可以开始训练了  
最简单的训练过程就以下两行代码

with step in xrange(FLAGS.max_Step)
        sess.run(train_op)

但是本例子要复杂一点，读入的数据每一步都要进行切分，以适应之前生成的place\_holder

#### (1).fill\_feed\_dict ####

先让image\_feed和labels\_feed去向dataset索要下一次训练的一个batchsize的数据

images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size,
                                                   FLAGS.fake_data)

再讲这个数据整合成一个python字典的形式，image\_placeholder 和labels\_placeholder作为字典的key， image\_feed和labels\_feed作为字典的value

feed_dict = {
        images_placeholder: images_feed,
        labels_placeholder: labels_feed,
    }

#### (2).检查训练状态 ####

see.run 在每一步训练之后都会取得两个值，loss 和train\_op，（train\_op不返回任何值，discard）  
，所以会得到每一步的loss  
每训练100次，check一下，输出loss  
每训练1000次，进行evaluation，将生成的model保存一下

#### (3).do\_eval ####

计算整个epoch的精度

true_count = 0  # Counts the number of correct predictions.
      steps_per_epoch = data_set.num_examples // FLAGS.batch_size
      num_examples = steps_per_epoch * FLAGS.batch_size
      for step in xrange(steps_per_epoch):
        feed_dict = fill_feed_dict(data_set,
                                   images_placeholder,
                                   labels_placeholder)
        true_count += sess.run(eval_correct, feed_dict=feed_dict)
      precision = float(true_count) / num_examples
      print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %
            (num_examples, true_count, precision))

## 代码 ##

### 建图步骤 ###

*  Generate placeholders for the images and labels.
 *  Build a Graph that computes predictions from the inference model.
 *  Add to the Graph the Ops for loss calculation.
 *  Add to the Graph the Ops that calculate and apply gradients.
 *  Add the Op to compare the logits to the labels during evaluation.
 *  Build the summary Tensor based on the TF collection of Summaries.
 *  Add the variable initializer Op.
 *  Create a saver for writing training checkpoints.
 *  Create a session for running Ops on the Graph.
 *  Instantiate a SummaryWriter to output summaries and the Graph.
 *  And then after everything is built:Run the Op to initialize the variables.  
    Start the training loop.

### 读取数据 ###

def run_training():
      """Train MNIST for a number of steps."""
      # Get the sets of images and labels for training, validation, and
      # test on MNIST.
      data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)

### 开始建图 ###

# Tell TensorFlow that the model will be built into the default Graph.
      with tf.Graph().as_default():
        # Generate placeholders for the images and labels.
        images_placeholder, labels_placeholder = placeholder_inputs(
            FLAGS.batch_size)
    
        # Build a Graph that computes predictions from the inference model.
        logits = mnist.inference(images_placeholder,
                                 FLAGS.hidden1,
                                 FLAGS.hidden2)
    
        # Add to the Graph the Ops for loss calculation.
        loss = mnist.loss(logits, labels_placeholder)
    
        # Add to the Graph the Ops that calculate and apply gradients.
        train_op = mnist.training(loss, FLAGS.learning_rate)
    
        # Add the Op to compare the logits to the labels during evaluation.
        eval_correct = mnist.evaluation(logits, labels_placeholder)
    
        # Build the summary Tensor based on the TF collection of Summaries.
        summary = tf.summary.merge_all()
    
        # Add the variable initializer Op.
        init = tf.global_variables_initializer()
    
        # Create a saver for writing training checkpoints.
        saver = tf.train.Saver()
    
        # Create a session for running Ops on the Graph.
        sess = tf.Session()
    
        # Instantiate a SummaryWriter to output summaries and the Graph.
        summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
    
        # And then after everything is built:
    
        # Run the Op to initialize the variables.
        sess.run(init)

### 开始循环训练 ###

# Start the training loop.
        for step in xrange(FLAGS.max_steps):
          start_time = time.time()
    
          # Fill a feed dictionary with the actual set of images and labels
          # for this particular training step.
          feed_dict = fill_feed_dict(data_sets.train,
                                     images_placeholder,
                                     labels_placeholder)
    
          # Run one step of the model.  The return values are the activations
          # from the `train_op` (which is discarded) and the `loss` Op.  To
          # inspect the values of your Ops or variables, you may include them
          # in the list passed to sess.run() and the value tensors will be
          # returned in the tuple from the call.
          _, loss_value = sess.run([train_op, loss],
                                   feed_dict=feed_dict)
    
          duration = time.time() - start_time
    
          # Write the summaries and print an overview fairly often.
          if step % 100 == 0:
            # Print status to stdout.
            print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
            # Update the events file.
            summary_str = sess.run(summary, feed_dict=feed_dict)
            summary_writer.add_summary(summary_str, step)
            summary_writer.flush()
    
          # Save a checkpoint and evaluate the model periodically.
          if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
            checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
            saver.save(sess, checkpoint_file, global_step=step)
            # Evaluate against the training set.
            print('Training Data Eval:')
            do_eval(sess,
                    eval_correct,
                    images_placeholder,
                    labels_placeholder,
                    data_sets.train)
            # Evaluate against the validation set.
            print('Validation Data Eval:')
            do_eval(sess,
                    eval_correct,
                    images_placeholder,
                    labels_placeholder,
                    data_sets.validation)
            # Evaluate against the test set.
            print('Test Data Eval:')
            do_eval(sess,
                    eval_correct,
                    images_placeholder,
                    labels_placeholder,
                    data_sets.test)

[TF]: https://www.tensorflow.org/get_started/mnist/mechanics
[TF 1]: http://www.tensorfly.cn/tfdoc/tutorials/mnist_tf.html