（四）Tensorboard学习——mnist_with_summaries.py

    Tensorboard是一个可视化工具，通过mnist_with_summaries.py这个文件可以对其有个很好的了解！

    我对其进行了比较详细的注释！

    这个网址的视频非常好，下面这个视频对这个文件有详细的讲解：

    http://v.youku.com/v_show/id_XMjczNjQzMjY5Mg==.html

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport sysimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataFLAGS = None #全局变量   # We can't initialize these variables to 0 - the network will get stuck.def weight_variable(shape):  """Create a weight variable with appropriate initialization."""  initial = tf.truncated_normal(shape, stddev=0.1)  return tf.Variable(initial)def bias_variable(shape):  """Create a bias variable with appropriate initialization."""  initial = tf.constant(0.1, shape=shape)  return tf.Variable(initial)def variable_summaries(var):  """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""  with tf.name_scope('summaries'):    mean = tf.reduce_mean(var)    tf.summary.scalar('mean', mean)    with tf.name_scope('stddev'):      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))    tf.summary.scalar('stddev', stddev)    tf.summary.scalar('max', tf.reduce_max(var))    tf.summary.scalar('min', tf.reduce_min(var))    tf.summary.histogram('histogram', var)#一个通用的用于构建一个layer层节点，且包含张量汇总def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):  """Reusable code for making a simple neural net layer.  It does a matrix multiply, bias add, and then uses relu to nonlinearize.  It also sets up name scoping so that the resultant graph is easy to read,  and adds a number of summary ops.  """  # Adding a name scope ensures logical grouping of the layers in the graph.  with tf.name_scope(layer_name):    # This Variable will hold the state of the weights for the layer    with tf.name_scope('weights'):      weights = weight_variable([input_dim, output_dim])      variable_summaries(weights)    with tf.name_scope('biases'):      biases = bias_variable([output_dim])      variable_summaries(biases)    with tf.name_scope('Wx_plus_b'):      preactivate = tf.matmul(input_tensor, weights) + biases      tf.summary.histogram('pre_activations', preactivate)    activations = act(preactivate, name='activation')    tf.summary.histogram('activations', activations)    return activations  def train():  # 加载数据  mnist = input_data.read_data_sets(FLAGS.data_dir,                                    one_hot=True,                                    fake_data=FLAGS.fake_data) #这个函数使用了上面的mnist,没有移动到外面  def feed_dict(train):  #这个train=true  or  false 不同情况  传入的数据不同    """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""    if train or FLAGS.fake_data: #训练数据      xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)#分批次读入      k = FLAGS.dropout  #训练的时候drop    else:      xs, ys = mnist.test.images, mnist.test.labels      k = 1.0      #测试的时候drop固定为1    return {x: xs, y_: ys, keep_prob: k}  #打开会话  sess = tf.InteractiveSession()    # 建立网络模型  # 输入节点  with tf.name_scope('input'):    x = tf.placeholder(tf.float32, [None, 784], name='x-input')    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')  # 输入变形，只用于可视化图像  with tf.name_scope('input_reshape'):    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])    tf.summary.image('input', image_shaped_input, 10)    #调用函数生成节点tf.name_scope('layer1') 且汇总里面的张量  hidden1 = nn_layer(x, 784, 500, 'layer1')   #dropout节点 并汇总scalar：keep_prob  with tf.name_scope('dropout'):    keep_prob = tf.placeholder(tf.float32)    tf.summary.scalar('dropout_keep_probability', keep_prob)    dropped = tf.nn.dropout(hidden1, keep_prob)  # Do not apply softmax activation yet, see below.  #调用函数生成节点tf.name_scope('layer2') 且汇总里面的张量  y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)   #交叉熵节点 里面还有total节点，汇总交scalar:叉熵的均值  with tf.name_scope('cross_entropy'):    # The raw formulation of cross-entropy,    #    # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),    #                               reduction_indices=[1]))    #    # can be numerically unstable.    #    # So here we use tf.nn.softmax_cross_entropy_with_logits on the    # raw outputs of the nn_layer above, and then average across    # the batch.    diff = tf.nn.softmax_cross_entropy_with_logits(y, y_)    with tf.name_scope('total'):      cross_entropy = tf.reduce_mean(diff)  tf.summary.scalar('cross_entropy', cross_entropy)  #训练节点  with tf.name_scope('train'):    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(        cross_entropy)#自适应优化器  #accuracy节点  里面有两个节点，最后汇总scalar：平均准确率  with tf.name_scope('accuracy'):    with tf.name_scope('correct_prediction'):      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    with tf.name_scope('accuracy'):      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  tf.summary.scalar('accuracy', accuracy)  #汇总所有节点  # Merge all the summaries and write them out to /tmp/mnist_logs (by default)  merged = tf.summary.merge_all()    #训练时  train_writer = tf.train.SummaryWriter(FLAGS.log_dir + '/train',                                        sess.graph) #这里不仅汇总节点，而且会生成计算图（因为有graph）  #测试时  test_writer = tf.train.SummaryWriter(FLAGS.log_dir + '/test') #仅汇总节点    #训练前初始化变量  tf.global_variables_initializer().run()  # Train the model, and also write summaries.  # Every 10th step, measure test-set accuracy, and write test summaries  # All other steps, run train_step on training data, & add training summaries  for i in range(FLAGS.max_steps):    if i % 10 == 0:  # 每10批数据 Record summaries and test-set accuracy      summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))#merged是汇总      test_writer.add_summary(summary, i)                                    #test_writer实例，将汇总写入test部分      print('Accuracy at step %s: %s' % (i, acc))    else:  # Record train set summaries, and train      if i % 100 == 99:  # Record execution stats        run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)        run_metadata = tf.RunMetadata()        summary, _ = sess.run([merged, train_step],                              feed_dict=feed_dict(True),                              options=run_options,                              run_metadata=run_metadata)           #merged是汇总        train_writer.add_run_metadata(run_metadata, 'step%03d' % i)#train_writer实例，将汇总写入train部分        train_writer.add_summary(summary, i)                       #train_writer实例，将汇总写入train部分,一定要加上i(即step)        print('Adding run metadata for', i)      else:  # Record a summary        summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))  #merged是汇总        train_writer.add_summary(summary, i)                     #train_writer实例，将汇总写入train部分,一定要加上i(即step)  train_writer.close()     #关闭实例  test_writer.close()      #关闭实例def main(_):  if tf.gfile.Exists(FLAGS.log_dir):    tf.gfile.DeleteRecursively(FLAGS.log_dir)  tf.gfile.MakeDirs(FLAGS.log_dir)  train()if __name__ == '__main__':  parser = argparse.ArgumentParser()  parser.add_argument('--fake_data', nargs='?', const=True, type=bool,                      default=False,                      help='If true, uses fake data for unit testing.')  parser.add_argument('--max_steps', type=int, default=1000,                      help='Number of steps to run trainer.')  parser.add_argument('--learning_rate', type=float, default=0.001,                      help='Initial learning rate')  parser.add_argument('--dropout', type=float, default=0.9,                      help='Keep probability for training dropout.')  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',                      help='Directory for storing input data')  parser.add_argument('--log_dir', type=str, default='/tmp/tensorflow/mnist/logs/mnist_with_summaries',                      help='Summaries log directory')  FLAGS, unparsed = parser.parse_known_args()  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)