通俗易懂之Tensorflow summary类 & 初识tensorboard

# Display the training images in the visualizer.
  tf.summary.image(‘images‘, images)

def _activation_summary(x):
  """Helper to create summaries for activations.

  Creates a summary that provides a histogram of activations.
  Creates a summary that measure the sparsity of activations.

  Args:
    x: Tensor
  Returns:
    nothing
  """
  # Remove ‘tower_[0-9]/‘ from the name in case this is a multi-GPU training
  # session. This helps the clarity of presentation on tensorboard.
  tensor_name = re.sub(‘%s_[0-9]*/‘ % TOWER_NAME, ‘‘, x.op.name)
  tf.summary.histogram(tensor_name + ‘/activations‘, x)
  tf.summary.scalar(tensor_name + ‘/sparsity‘, tf.nn.zero_fraction(x))

_activation_summary(conv1)
_activation_summary(conv2)
_activation_summary(local3)
_activation_summary(local4)
_activation_summary(softmax_linear)

def _add_loss_summaries(total_loss):
  """Add summaries for losses in CIFAR-10 model.

  Generates moving average for all losses and associated summaries for
  visualizing the performance of the network.

  Args:
    total_loss: Total loss from loss().
  Returns:
    loss_averages_op: op for generating moving averages of losses.
  """
  # Compute the moving average of all individual losses and the total loss.
  loss_averages = tf.train.ExponentialMovingAverage(0.9, name=‘avg‘)
  losses = tf.get_collection(‘losses‘)
  loss_averages_op = loss_averages.apply(losses + [total_loss])

  # Attach a scalar summary to all individual losses and the total loss; do the
  # same for the averaged version of the losses.
  for l in losses + [total_loss]:
    # Name each loss as ‘(raw)‘ and name the moving average version of the loss
    # as the original loss name.
    tf.summary.scalar(l.op.name +‘ (raw)‘, l)
    tf.summary.scalar(l.op.name, loss_averages.average(l))

  return loss_averages_op

tf.summary.scalar(‘learning_rate‘, lr)
# Add histograms for trainable variables.
  for var in tf.trainable_variables():
    tf.summary.histogram(var.op.name, var)
# Add histograms for gradients.
  for grad, var in grads:
    if grad is not None:
      tf.summary.histogram(var.op.name + ‘/gradients‘, grad)

# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()

summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

if step % 100 == 0:
        summary_str = sess.run(summary_op)
        summary_writer.add_summary(summary_str, step)

model_checkpoint_path: "model.ckpt-1000"
all_model_checkpoint_paths: "model.ckpt-0"
all_model_checkpoint_paths: "model.ckpt-1000"

if step % 100 == 0:
        summary_str = sess.run(summary_op)
        summary_writer.add_summary(summary_str, step)

tf.app.flags.DEFINE_string(‘checkpoint_dir‘, ‘cifar10_train/‘,
                           """Directory where to read model checkpoints.""")

def eval_once(saver, summary_writer, top_k_op, summary_op):
  """Run Eval once.

  Args:
    saver: Saver.
    summary_writer: Summary writer.
    top_k_op: Top K op.
    summary_op: Summary op.
  """
  with tf.Session() as sess:
    ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
    if ckpt and ckpt.model_checkpoint_path:
      # Restores from checkpoint
      saver.restore(sess, ckpt.model_checkpoint_path)
      # Assuming model_checkpoint_path looks something like:
      #   /my-favorite-path/cifar10_train/model.ckpt-0,
      # extract global_step from it.
      global_step = ckpt.model_checkpoint_path.split(‘/‘)[-1].split(‘-‘)[-1]
    else:
      print(‘No checkpoint file found‘)
      return

def evaluate():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default():

tf.app.flags.DEFINE_string(‘checkpoint_dir‘, ‘cifar10_train/‘,
                           """Directory where to read model checkpoints.""")
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path +‘.meta‘)
with tf.Session() as sess:
    saver.restore(sess,ckpt.model_checkpoint_path)