TensorFlow学习笔记（7）----TensorBoard_2

比之前的例子更加庞大，使用全连接识别MNIST，需要命名空间更多，程序更灵活，但基本的函数换是那些。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '
                     'for unit testing.')
flags.DEFINE_integer('max_steps', 1000, 'Number of steps to run trainer.')
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_float('dropout', 0.5, 'Keep probability for training dropout.')
flags.DEFINE_string('data_dir', '/tmp/data', 'Directory for storing data')
flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')

# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir,one_hot=True,fake_data=FLAGS.fake_data)
sess = tf.InteractiveSession()

# 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, name):
  """Attach a lot of summaries to a Tensor."""
  with tf.name_scope('summaries'):
    mean = tf.reduce_mean(var)
    tf.scalar_summary('mean/' + name, mean)
    with tf.name_scope('stddev'):
      stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
    tf.scalar_summary('sttdev/' + name, stddev)
    tf.scalar_summary('max/' + name, tf.reduce_max(var))
    tf.scalar_summary('min/' + name, tf.reduce_min(var))
    tf.histogram_summary(name, var)

def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
  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, layer_name + '/weights')
    with tf.name_scope('biases'):
      biases = bias_variable([output_dim])
      variable_summaries(biases, layer_name + '/biases')
    with tf.name_scope('Wx_plus_b'):
      preactivate = tf.matmul(input_tensor, weights) + biases
      tf.histogram_summary(layer_name + '/pre_activations', preactivate)
    activations = act(preactivate, 'activation')
    tf.histogram_summary(layer_name + '/activations', activations)
    return activations

# Input placehoolders
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')

keep_prob = tf.placeholder(tf.float32)

def feed_dict(train):
  """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
  if train or FLAGS.fake_data:
    xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
    k = FLAGS.dropout
  else:
    xs, ys = mnist.test.images, mnist.test.labels
    k = 1.0
  return {x: xs, y_: ys, keep_prob: k}

def train():

  with tf.name_scope('input_reshape'):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.image_summary('input', image_shaped_input, 10)

  hidden1 = nn_layer(x, 784, 500, 'layer1')


  with tf.name_scope('dropout1'):
    tf.scalar_summary('dropout_keep_probability1', keep_prob)
    dropped1 = tf.nn.dropout(hidden1, keep_prob)

  hidden2 = nn_layer(dropped1, 500, 300, 'layer2')

  with tf.name_scope('dropout2'):
    tf.scalar_summary('dropout_keep_probability2', keep_prob)
    dropped2 = tf.nn.dropout(hidden2, keep_prob)

  y = nn_layer(dropped2, 300, 10, 'layer3', act=tf.nn.softmax)

  with tf.name_scope('cross_entropy'):
    diff = y_ * tf.log(y)
    with tf.name_scope('total'):
      cross_entropy = -tf.reduce_mean(diff)
    tf.scalar_summary('cross entropy', cross_entropy)

  with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
        cross_entropy)

  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.scalar_summary('accuracy', accuracy)

  # Merge all the summaries and write them out to /tmp/mnist_logs (by default)
  merged = tf.merge_all_summaries()
  train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train',sess.graph)
  test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test')
  tf.initialize_all_variables().run()

  #
  for i in range(FLAGS.max_steps):
    if i % 10 == 0:  # Record summaries and test-set accuracy
      summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
      test_writer.add_summary(summary, i)
      print('Accuracy at step %s: %s' % (i, acc))
    else:  # Record train set summaries, and train
      summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
      train_writer.add_summary(summary, i)


def main(_):
  if tf.gfile.Exists(FLAGS.summaries_dir):
    tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
  tf.gfile.MakeDirs(FLAGS.summaries_dir)
  train()


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

<span style="font-size:14px;">variable_summaries</span>

函数可以用在其他地方，打开目录定位/tmp/mnist_logs图表中可以有两条曲线，一个是是训练一个是测试

注意：如果保存数据过于频繁，会显著增加运行时间！毕竟硬盘读取的速度太慢，即使是SSD也不必要全部保存数据（程序速度能快一点是一点），一般的做法是每个100---1000步保存一下数据供图表显示即可。