How can I separate runs of my TensorFlow code in TensorBoard?

Question

My TensorBoard plots treat successive runs of my TensorFlow code as if they are all part of the same run. For example if I first run my code (below) with FLAGS.epochs == 10 and then rerun it with FLAGS.epochs == 40 I get

which "loops back" at the end of the first run to begin the second.

Is there a way to treat multiple runs of my code as distinct logs, that, for example, could be compared or individually viewed?

---

from __future__ import (absolute_import, print_function, division, unicode_literals)

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# Basic model parameters as external flags.
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('epochs', 40, 'Epochs to run')
flags.DEFINE_integer('mb_size', 40, 'Mini-batch size. Must divide evenly into the dataset sizes.')
lags.DEFINE_float('learning_rate', 0.15, 'Initial learning rate.')
flags.DEFINE_float('regularization_weight', 0.1 / 1000, 'Regularization lambda.')
flags.DEFINE_string('data_dir', './data', 'Directory to hold training and test data.')
flags.DEFINE_string('train_dir', './_tmp/train', 'Directory to log training (and the network def).')
flags.DEFINE_string('test_dir', './_tmp/test', 'Directory to log testing.')

def variable_summaries(var, name):
    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, neuron_fn, layer_name):
    with tf.name_scope(layer_name):
        # This Variable will hold the state of the weights for the layer
        with tf.name_scope("weights"):
            weights = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1))
            variable_summaries(weights, layer_name + '/weights')
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.constant(0.1, shape=[output_dim]))
            variable_summaries(biases, layer_name + '/biases')
        with tf.name_scope('activations'):
            with tf.name_scope('weighted_inputs'):
                weighted_inputs = tf.matmul(input_tensor, weights) + biases
                tf.histogram_summary(layer_name + '/weighted_inputs', weighted_inputs)
            output = neuron_fn(weighted_inputs)
            tf.histogram_summary(layer_name + '/output', output)
    return output, weights 

# Collect data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

# Inputs and outputs
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

# Network structure
o1, W1 = nn_layer(x, 784, 30, tf.nn.sigmoid, 'hidden_layer')
y, W2 = nn_layer(o1, 30, 10, tf.nn.softmax, 'output_layer')

with tf.name_scope('accuracy'):
    with tf.name_scope('loss'):
        cost = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
        loss = cost + FLAGS.regularization_weight * (tf.nn.l2_loss(W1) + tf.nn.l2_loss(W2))
    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)
    tf.scalar_summary('loss', loss)

train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(loss)

# Logging
train_writer = tf.train.SummaryWriter(FLAGS.train_dir, tf.get_default_graph())
test_writer = tf.train.SummaryWriter(FLAGS.test_dir)
merged = tf.merge_all_summaries()

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())

    for ep in range(FLAGS.epochs):
        for mb in range(int(len(mnist.train.images)/FLAGS.mb_size)):
            batch_xs, batch_ys = mnist.train.next_batch(FLAGS.mb_size)
            sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

        summary = sess.run(merged, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
        test_writer.add_summary(summary, ep+1)

Answer 1

You can put your runs into separate subdirectories, like:

./logdir/2016111301/
./logdir/2016111302/
./logdir/2016111401/

than you call your tensorboard function on the root dir:

tensorboard --logdir=logdir

Than you will have the separate logfiles like: